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entraJ S 'L'bc1ence'Irary APR 131987lJnive of r-, .'-,Of/daPotentialEvapotranspirationProbabilitiesAndDistributionsinFloridaI,)\ M-qrm ---I l \niP.'11.1I?lr.--J __ .:.:...llJ'_':_ltJ_1 ... BE:u:-lIe_tin_2--,o5_,,-,J),.IiA.G.Smajstrla,G.A.Clark,S.F. Shih,F.Z.Zazueta, andD.S.HarrisonFloridaCooperativeExtensionServiceInstituteofFoodandAgriculturalSCiencesUniversityofFlorida,GainesvilleJohnT.Woeste, DeanforExtensionUNIVERSITY OF FLORICAHUME LIBRARYAUG 08 198) l.F.A.S.Univ.ofFlorida

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PotentialEvapotranspirationProbabilitiesandDistributionsinFloridaA.G.Smajstrla,G.A.Clark,S.F.Shih,F.S.ZazuetaandD.S.Harrison*INTRODUCTIONPotentialevapotranspiration(ETp)istherateatwhichwater,ifavailable,wouldberemovedfromthesoilandplantsurfacesbyevaporationandtranspiration.Theconceptofpotentialevapotranspirationimpliesthattheplantcanopyisactivelygrowing andabletosupplywatertosatisfytheclimaticdemand.Energymustbeavailabletomovewatermoleculesfromtheliquidtothevaporstate.Thus,thecalculationofETpisnormallybasedatleastpartiallyonanenergybalanceattheplantcanopy.Astheavailableenergyattheplantcanopyincreases,sodoesthepotentialtoevaporatewater.ThusETpisanindexofavailableenergyattheplantcanopy.*AssociateProfessor,FormerGraduateResearchAssistant,Professor,VisitingAssistantProfessor,andProfessor,respectively,AgriculturalEngineeringDepartment,InstituteofFood andAgriculturalSciences,UniversityofFlorida,Gainesvi11e,FL32611

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Penman(1948)developedanequationtoestimatethepotentialrateofevapotranspirationfromanextensive,shortgrasscovercompletelyshadingtheground andadequatelysuppliedwithwater.ThisequationanditslatermodificationshavebeenwidelyacceptedasanindexofETp(Joneset al., 1984).Penman'sequationwasdevelopedtoestimatewaterusebyanactively-growingshort,greengrasscropwithanadequatewatersupply.ItcalculatesETpdirectlyforthoseconditionsonly.Forothercropsandconditions,Penman'sequationisonlyanindexofclimaticdemand.It can, however,beusedtocalculateactualevapotranspiration(ET)ratesforothercropsorconditionsifitiscalibratedforthoseconditions.Calibrationrequiresthedeterminationofappropriatecoefficientswhicharespecificforthecropandconditionsbeingstudied.Thesecoefficientsaretypicallycalledcropcoefficients.TheyaremultipliedbytheETptoproducetheactualcropTfortheconditionsbeingstudied.For mostcropswithcompletevegetativecanopies,cropcoefficientsaretypicallynear1.0duringpeakwateruseperiods.That is, actualETratesarenearETpratesfortheseconditions.Becausepeakwateruseratesareoftenalsothecriticalwateruseperiodsintermsofeconomicyields,ETpratesaspredictedbythePenmanequationaregoodindicatorsoftheratesofcropwateruseduringthesecriticalperiods.ETpratesascalculatedbythePenmanequationalsoprovideareferenceforthecomparisonofclimaticconditionsfromonelocationtoanother.Thus,ifactualETratesareknownforaspecificcropandculturalconditionsat2

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onelocation,ETratescanbepredictedforthesamecropandconditionsatanotherlocationifETpratesarecalculatedatbothlocations.POTENTIALETCALCULATIONSInthiswork,thePenmanequationwasusedtocalculateETpforninelocationsinandaroundFlorida,using25yearsofhourlyweatherrecords(1952-1976)obtainedfromtheNationalWeatherServiceStationsatthoselocations.TheninelocationswereMobile,Alabama(MOB),andApalachicola(APL),Tallahassee(TAL),Jacksonvi11e (JAX), Daytona Beach(DTB),0r1 and0(0 RL),Tampa(TAM),WestPalmBeach(WPB), and Miami (MIA)Florida.Thoselocationsareshowngraphicallyinfigures1-15.Hourlyweatherrecordswereaveragedortotaledtoproducedailyvalueswhich werethenusedwiththePenmanequationaspresentedbyJoneseta1.(1984)tocalculatedailyETp.Acompletedocumentationofthetechniqueused andlistsofparameterswerepresentedbyClarkandSmajstr1a(1982).DailyETpdataweresummedtoproducemonthly,seasonal,andannualvaluesinTable1.Meanand medianvaluesandtheirstandarddeviationsandcoefficientsofvariationwerealsocalculatedfortheninelocations.BothmonthlyandaveragedailyETpdataaregiven.Averagedailyvaluesweredeterminedbydividingthemonthlyvaluesbythenumberofdayspermonth.MonthlyvalueswerealsosummedtoobtainseasonalandannualETpvalues.Winterseasonalvaluesarethetota1sforthemonthsofOctoberthroughMarch.Summerseason a1valuesarethetota1sforthemonthsofAprilthroughSeptember.3

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MONTHLYETpDISTRIBUTIONSFigure16 showsmonthlyETpdistributionsfromTallahasse,Orlando,and WestPalmBeach.ThesecurvesrepresenttheextremesinETprangesinFlorida.Figure16 showsthatthepeakETpmonthinpeninsularFloridaisMay.ETpinJuneandJulyislowerthanMayduetocloudcoverwhichaccompaniesnormalsummerrainyperiods.InnorthFlorida,thepeakETpmonthistypicallyJune,althoughthereisverylittledifferenceinMay,June,andJulymonthlyETp.Table1orFigure16showsthatETprangesfromapproximately2inches/monthinDecembertoabout6inches/monthinthemonthsofMay-July.LowestannualETpvaluesoccuratTallahasseeinnorthFlorida,whereasgreatestannua1ETpvaluesoccuratWest Palm BeachinsouthFlorida.However,greatestmonthlyETpvaluesoccurinMayatOrlandoincentralFlorida.POTENTIALETPROBABILITIESMonthly,seasonal,andannualETpprobabilitiesaregiveninTable2.Probabilitieswerecalculatedbasedona normaldistributionofdata.Thisdistributionwasgraphicallydeterminedtobevalid.Anotherindicationofthenormaldistributioncanbeobservedfromthecloseagreementbetweenthemean andmedianvaluesforthemonthly,seasonal,andannualETpdatainTable1.Table2 showstheprobabilitiesthatagivenmonthlyETpwillnotbeexceeded.Forexample,the80%ETpforJanuaryis2.104

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(INCHES)JANUARYMONTHLYPOTENTIALETALADATAENTRYUPPERMIDDLELOWERPROBABILITY 50'0/080%,95%M'UI WPB.Figure1.JanuaryMonthly Potential ET.(INCHES)Figure2.FebruaryMonthlyPotential ET.WPB. lICALlIM M'U:.0'10 10"40--=-80% 95 'Y. PROBABILITY50%DATAENTRYUPPERMIDDLELOWER2.62.93.2FEBRUARYMONTHLYPOTENTIALETALA.5

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(INCHES)Figure3.MarchMonthlyPotential ET.MARCHMONTHLYPOTENTIALETWPB. teALl! I" MIUI95%50%BD"Io PROBABILITYDATAENTRYUPPER MIDDLE LOWER3.64.0 4.4ALA.(INCHES)Figure4.AprilMonthlyPotentialET.APRILMONTHLYPOTENTIALETWPB. ItAl.IItI.11." OSlO10 3040-=-=PROBABILITYDATAENTRYUPPER MIDDLE LOWER5.0 5.2 5.4 ALA.6

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(INCHES)Figure5.May Monthly Potential ET.MAYMONTHLYPOTENTIALETWP8.6.36.6eORL.7.0 !tCAUIMM'UI051010304095%80% PROBABILITY 50% DATAENTRYUPPERMIDDLELOWER5.76.06.2ALA.(INCHES)Figure6.JuneMonthly Potential ET.JUNEMONTHLYPOTENTIALETWP8. KAL!'MMIlII80%95% 50%PROBABILITYDATAENTRYUPPER MIDDLE LOWER5.86.26.5ALA.7

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(INCHES)Figure7.JulyMonthlyPotentialET.JULYMONTHLYPOTENTIALETWPB. teal......n50e;.800;. 95"I.PROBABILITY DATA ENTRYUPPERMIDDLELOWER5.55.B6.1ALA.(INCHES)KEYDATA ENTRY PROBABILITYAUGUSTMONTHLYPOTENTIALETWPB. ICAL.'"....0 80%95"1.UPPERMIDDLELOWER5.25.65.9ALA.Figure8.AugustMonthlyPotentialET.8

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(INCHES)KEYDATAENTRYPROBABILITYUPPER50%SEPTEMBERMONTHLYPOTENTIALETWPB. KALa'''.''.1U:.rV800/.950/. MIDDLELOWER4.54.85.1ALA.Figure9.SeptemberMonthlyPotential ET.(INCHES)KEYDATAENTRYPROBABILITYOCTOBERMONTHLYPOTENTIALETWPB..' ,.., 0" teALlllIIlIllLn500/. 80/.95 'Y. UPPERMIDDLELOWER3.84.1 4.4 ALA.Figure10.OctoberMonthlyPotentialET.9

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(INCHES)Figure 11. November Monthly PotentialET.NOVEMBERMONTHLYPOTENTIALETWPB. 50%80% 95%PROBABILITYDATAENTRYUPPER MIDDLE LOWER2.72.93.1ALA.(INCHES)Figure 12. December Monthly PotentialET.DECEMBERMONTtiLYPOTENTIALETWPB. ICAU... IIIIUS 40-=-=-500;.80%950/. PROBABILITYDATAENTRYUPPER MIDDLE LOWER2.0 2.2 2.4ALA.10

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APRIL-SEPTEMBER(INCHES)Figure 13.SummerPotentialET.WPB. ;n eORL35.7 50%800/.95'Y. PROBABILITYDATAENTRyUPPERMlDOLELOWERSUMMERPOTENTIALETFigure 14. Winter Potential ET.WINTERPOTENTIALETOCTOBER MARCHWPB.PROBABILITY 50'Y.80% DATAENTRY UPPER MIOOLELOWER(INCHES)ALA.11

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175Figure 15. Annual Potential ET.ANNUALPOTENTIALETWPB. ICALI11IIIIILIS50'-.eO-k95.". (INCHES)UPPERMIDDLELOWER .!5.ll DATAENTRYPROBABILITYMONTHLY ETpDISTRIBUTIONS -0TALLAHASSEE -0ORLANDO WESTPALMBEACH150 -.s::.c:0E125 ..... E ..s z0 1000:: a::Ul Z oCt 0:: tOa. oCt >W -l 50 oCti= zW tOa.25JFigure16.FMAMJJMONTHMonthly Potential ET Distribution!\, 12A SoND

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TABLE 1.STATISTICALCHARACTERISTICSOFPOTENTIALET------------------------------------------------------------------MEANDAILYMONTHLYMONTHLYETSTANDARDCOEFFICIENTMONTHETMEANETMEDIANETDEVIATIONOFVARIATION(inches)
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TABLE1(CONTINUED)STATISTICALCHARACTERISTICSOFPOTENTIALET------------------------------------------------------------------MEANDAllYMONTHLYMONTHLYfTSTANDARDCOEFFICIENTMONTHEr MEAN ET MEDIAN ETDEVIATION OF VARIATION(inches) (inches) (inches) (--) 0.15 0.15 0.13 0.06 0,060.08 0.060.09 0,09 0.10 0.09 0.10 0.30 0.38 0.470.300.330.44 0.34 0.440.42 0.360.250.210.0650.091 0.117 0.163 0.184 0.193 0.177 0.167 0.1480.1220.0890.066123 45 678 '1 101112 1'"::'LtGZI..,r-JII;rl..) MIAMI,FLORIDA10.1003.093.130.260.08 0.1313.663.700.280.0830.1574.884.940.410.0840.1915.74 5.89 0.360.06 5 0.188 5.835.83 0.560.10 6 0.176 5.29 5.420.380.0770.1835.66 5.65 0.410.0780.171 5.315.35 0.380.0790.1524.574.550.360.08100.1384.274.350.380.09110.1143.433.440.270.08120.0962.993.020.320.11-----------_._-----------------------------------------------------SUMMER0.17732.39 32.55 1.570.05WINTER0.12322.3222.231.240.06ANNUAL0.15054.71 54.95 2.430.04 .. --------------------------------Q---,{-,-:-/----,:::;.--:,-.--,-W<,0 i.. ;.,.'Il..'-",',MOBILE, 2.022.09 2.55 2.473.643.844.894.93 3.693.645.805.72 5.495.50 5.195.16 4.454.513.773.742.662.682.042.06------------------------------------------------------------------SUMMER 0.172 31.5131.531.220.04WINTER0.09216.6816.971.370.08ANNUAL 0.13248.1948.14 2.30 0.05 -----------------------------------------------------------------'1GWn '/ I \ l'( \ J' ORLANDO, FLORIIIA 10.0872.712.740.160.0620.1193.333.290.230.0730.1494.604.640.340.074 0.187 5.625.670.33 0.065 0.2036.286.380.430.076 0.1955.85 5.900.350.067 0.1895.85 5.840.250.048 0.1715.31 5.350.390.0790.1554.64 4.65 0.390.08100.1294.01 4.15 0.390.10110.1013.033.030.260.09120.0812.502.52 0.17 0.07 SUMMER 0.18333.5433.811.300.04WINTER0.11120.1820.44 0.960.05ANNUAL 0.147 53.7254.172.050.0414

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0.070.070.070.070.09 0.060.070.060.07..0.090.09 0.0:7 0.140.180.250.310.470.360.350.320.30 0.330.220.120.0640.0920.120O. 1 0.177 0.184 0.1700.1610.144 0.113 0.0790.05734 56 7 8 9101112MONTHTABLE1(CONTINUED)STATISTICALCHARACTERISTICSOFPOTENTIALET------------------------------------------_.__._---------------------MEANDAILYMONTHLYMONTHLYETSTANDARDCOEFFICIENTETMEANETMEDIANETDEVIATIONOFVARIATION (inches) (inches) (u,,:hes)(inches) (--)--..----_.-----1,0n;,--:;n---Lj""":-: ------------------TALLAHASSEE,FLORIDA1.98 1.96 2.57 2.57:l.?2 3.75 -%.75 4.715.485.37 5.53 5.555.27 5.29 4.995.014.334.313.513.522.362.361.76 1.75SUMMERWINTER ANNUAL 0.166 0.0870.12730.3415.9146.25 15.75 46.17 1.420.752.030.050.05 0.04i23 -45 67891011120.0870.117 0.1440.185 0.203 0.1960.1830.172 0.160 0.139 0.106 0.084 ;7 TAMPA,2.703.274.48 5.56 6.29 5.87 5.68 5.324.804.323.182.61 <;GN FLORIDA2.713.314.555.466.325.945.73 5.31 4.80 3.152.550.160.250.410.340.340.270.310.29 0.28 0.39 0.26 0.240.060.080.090.060.050.050.050.050.06 0.090.08 SUMMER WINTER ANNUAL0.183 0.1130.148 33.5220.56 54.09 33.4420.80 53.761.12 1.21 2.16 0.030.060.04 WESTPALMBEACH,FLORIDA10.1003.103.120.180.0620.1283.593.580.260.0730.1564.854.96 0.32 0.0740.1915.73 5.79 0.320.06 5 0.1935.98 5.85 0.520.0960.1795.375.430.340.0670.1845.725.710.370.0680.1755.435.520.270.0590.1554.654.630.390.08100.1414.364.440.500.11110.1173.523.570.270.08120.0993.073.050.280.09------------------------------------------------------------------SUMMER0.18032.8732.731.150.04WINTER0.12422.4822.551.060.05ANNUAL0.15255.3555.421.800.0315

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TABLE2.MONTHLYPOTENTIAL ET(inches) CALCULATED BY THEPENMANMETHOD. PROBABILITY LEVELATWHICHPOTENTIALETWILL NOTBE EXCEEDED.APLACHICOLA,FLORIDAMONTH12 :5 4 5(; 7 a 9 10 11 12SUMMER WINTER ANNUAL50 i.31.4 16.247.660 i.31.7 16,4 48.1 70 i. 32.016.748.580 i. 2.122.773.914.996.07 6.025.795.504.713.87 2.67 32.316.949.190 i. 2.212.884.07 J.12 6.216.17 3.945.70 4.843.972.762.07 95i. 2.282.974.215.226.336.296.075.864.954.062.832.1133.217.6 50.t. 1 '""3 45 i, 78 9101112SUMMER WItHER ANNUAL32.018.650.6[IAYTONABEACH,2.472.503.11 3.17 4.304.395.355.445.956.075.645.745.65 5.7:2 5.24 5.32 4.614.693.944.022.822.872.272.3132.519.0 51.3 FLORIDA 2.55 3.254.495.536.205.85 :'i.79 5.414.774.112.94 2.3632.719.2 51.7 33.519.852.812 34 0:"i, 78 9 101112JACKSONVILLE,2.282.323.02 3.10 4.284.365.325.396.116.276.016.135.986.095.505.614.524.613.623.682.642.702.072.11FLORIDA2.373.194.475.496.466.286.215.744.713.752.762.172.443.314.615.616.726.496.375.934.853.852.85 2.25 2.503.414.735.726.936.666.516.074.973.942.922.31SUMI1ERWINTERANNUAL32.817.650.433.217.8 51.0 33.718.051.61634.218.352.434.918.753.535.619.054.4

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TA8LE2
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TABLE2(CONTINUED)MONTHLYPOTENTIAL CALCULATEDBYTHEPENMANMETHOD.,PROBABILITYLEVELAT'WHICHPOTENTIALETWILL NOTBE EXCEEDED. MONTHSOf. 60 7. 70 7. 80 7. 90 7. TALLAHASSEE, rLORIDA 11.982.022.062.102.172.22 "' 2.57 2.62 2.662.722.802.86 033.72 3.78 3.85 3.934.04 4013 44. 4.834.91 5.01" 5.48 5.60 5.725.876.086.25. ."6 5.535.625.72 5.83 5.996.12 "7 5.275.36 5.455.56 5.725.85 a 4.995.075.165.26 5.40c-c-.,""'t,JL 9 4.33 4.4('4.48 4.58 4.714.8210 3.51 3.603.683.793.934.0511 2036 2.412.47 2.54 2.64 2.72 121. ?6 1.79 1.82 1.86 1. 911 J-------------------------------------------------------------SUMMER 30.330.7 310131.532.2 32.7WINTER15.916.116.316.516.9 17.1 ANNUAL46.346.84;'.348.0 48.949.6-------------------------------------------------------------TAMPA,FLORIDA12.702.742.782.832.90 2.96 ., 3.273.333.403.48 3.59 3.68 034.48 4.584.704.83 5.015>16 4 5.56 5.65 5.745.84 5.99 6.11::;6.296.376.466.576.726.85 5.875.946.016.106.21 6.3175.685.76 5.855.94 6.086.1985.325.395.475.56 5.69 5.7994.80 4.874.945.035.165.26 10 4.324.424.53 4.65 4.824.97113.183.253.323.403.513.61122.612.682.742.822.933.01------------------------------------------------------------SUMMER 33.533.834.134.535.0 35.4 WINTER20.620.921.2 21.6 22.122.6ANNUAL 54.1 54.655.2 55.9 56.957.6WESTPALMBEACH, FLORIDA 13.10 3.153.203.263.343.4023.59 3.65 3.723.813.924.0134.85 4.935.01'5.11 5.25 5.3745.735.815.906.006.146.26 55.98 6.11 6.25 6.426.656.8465.375.45 5.55 5.65 5.805.927 5.72 5.815.916.036.196.3385.435.505.575.665.785.879 4.65 4.744.854.975.145.28104.364.48 4.624.785.005.18113.523.593.673.75 3.873.97123.07 3014 3.213.303.42 3.52SUMMER WINTERANNUAL32.922.555.333.222.755.833.523.056.31834.323.857.734.824.258.3

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inchesatTallahassee,Florida.Thesedatashouldbeinterpretedasfollows:in8yearsoutofevery10,themonthlyETpwou1dnotbeexpectedtoexceed2.10inches.Alternatively,inonly2yearsoutof10wouldthemonthlyETpbeexpectedtobegreaterthan2.10inches.InTable2,onlytheextremelargevaluesofETparepresented.Thesewou1dbemostusefultoirrigationsystemdesignersorothersconcernedwiththemaximumexpectedratesofwaterusebycrops.ExtremesmallvaluesofETpwould beofusetowaterresourcesplannersorothersconcernedwiththehydrologicbalanceduringextremewetaswellasextremedryyears.ExtremesmallvaluesofETpcanbecalculatedfromtheextremelargevaluesandthemeanETpbecauseofthesymmetryofthenormaldistribution.Forexample,tocalculatethe 5% probability(thatis,theETpthatwouldbeexpectedtobeexceededin95yearsoutof100)annualETpforMiami,subtractthedifferencebetweenthe 95% ETpvalueandthemeanfromthemean.ForMiami,the 95% ETpis61.9inches,themeanETpis54.7inches,andthedifferenceis7.2inches.The 5% ETpisthen54.7-7.2=47.5inches.Likewise,the 10% ETpwouldbecalculatedas49.1inchesusingthe90%andthemeanETpvalues.Thisprocedurecouldbe usedformonthlyorseasonalvaluesalso.PotentialETprobabilitydataandtheir distributionsareshowngraphicallyinfigures1-15.Thefirst12figuresshowmonthlyvalues.Figures13and14showwinterand summervalues,respectively.Figure15showsthedistributionoftotalannualETpinFlorida.19

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IneachFigure,3 ETpvaluesaregivenateachlocation.Ineachcasetheuppernumberisthe50%probability(orlong-termaverage)value,themiddlenumberisthe80%probabilityvalue,andthelowernumberisthe95%probabilityvalue.GEOGRAPHICALDISTRIBUTIONSOFETpFigures1-15allowtheuserofthesedatatoobservegeographicalinfluenceson ETp.Specifically,forseasonalETp,itcanbeseenthatthereislittlevariability(approximately10%)inthesummerdatathroughoutthestate.However,considerablevariability(approximately30%)occursduringthewintermonthswhenETpvaluesremainmuchlargerinsouthFloridathaninnorthFlorida.TheuserofthesedatacanestimateETpvaluesforotherlocationsinFloridabyinterpolatingbetweenthelocationsgiveninfigures1-15.USEOFPOTENTIALETPROBABILITYDATAHistorically,manysystemswhichareinfluencedbyclimaticparametershavebeendesignedforlong-termaverageortypicalclimaticconditionsatalocation.Amongtheseareagriculturalirrigationsystems,drainagesystems,andwaterallocationsbywatermanagementdistricts.Inmanycasesthesesystemsareinfluencedmorebycertainextremeorcriticalconditionsthanlong-termaverages.Forexample,onecouldconcludethatirrigationsystemsarenotrequiredinFloridabyobservingonlylongtermaveragerainfall.Actually,short-termdroughtsarecriticaltotheeconomicproductionofmanycropsgrowninthestate.20

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Inthiscaseandinotherswhereclimaticinfluencesareimportant,theprobabilitiesofoccurrenceofextremeeventsisa moremeaningfulconsiderationforsystemdesign.InthisworktheprobabilitiesofoccurrenceofextremesinETprateshavebeencalculated.Itissuggestedthatthesevalueswillbeusefulindesignand managementofirrigationsystems.Inaddition,watermanagementdistrictsmayfindthisinformationusefulfortheprocessofallocationofwatertoagriculturalusers.Asaspecificexample,irrigationsystemscanbedesignedforaspecificgreaterprobabilityofsuccessandfailureiftheyaresizedtosupplywaterforextremeuseconditionsratherthanonlyaverageconditions.ACTUALEVAPOTRANSPIRATIONInthiswork,onlypotentialevapotranspirationrateshavebeencalculated.ActualevapotranspirationforagivencropmaybegreaterorlessthanETpdependinguponthenatureofthecropanditsstageofgrowth.Todeterminetheactualevapotranspirationforaspecificcrop,ETpmustbemutlipliedby acropcoefficientwhichdependsuponthecropspeciesandstageofgrowth.Cropcoefficientsformanycropswerepresentedby andPruitt(1977). CropcoefficientsexpresstherelationshipbetweentheactualETandETpforaspecificcrop.TheplantsurfacereferencedbyETpdependsuponhowtheETpequationwasoriginallyderivedandcalibrated.ForthePenmanequationtheplantsurfacereferencedisthepreviously-discussedshortgrasscover.Thus,forthePenmanequa-21

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tion,cropcoefficientsexpresstheratiooftherelativewateruseofaspecificcropandconditionstothatofawell-wateredshortgreengrassreferencecrop.AlthoughactualETratesforspecificcropsarenotpresentedinthispublication,potentialevapotranspirationratesascalculatedbythePenmantechniqueareusefulbecausetheyprovideareferenceforthecomparisonofclimaticconditionsfrom onelocationtoanother.Forexample,iftheactualETduringAprilforacropgrownnearOrlandowas4.0inches,theactualETcanbecalculatedforthesamecropnearTallahasseeusingtheETpdatainthispublication.FirstcalculatetheratioofETpatthenewlocation(Tallahassee,inthisexample)totheETpatthelocationwhereactualcropETisknown(Orlando).Inthisexamp 1e,theApri 1ETpforTa11ahaseeis4.75inches(Table1),andtheAprilETpforOrlandois5.62inches.Theratioisthen4.75/5.62=0.845.Toca1 cu 1atetheestimatedactualETatTallahassee,multiplytheactualETknownforOrlandobythepreviouslycalculatedratio.Inthisexample,(4.0inches)(0.845)=3.38incheswouldbetheestimatedactualETforAprilatTallahassee.Asanotherexample,iftheactualETduringAprilforthepreviously-discussedcropnearOrlandowas4.0inches,thentheactualETnearTampawillalsobeapproximately4.0inches.Thiscan bedeterminedbyobservingthattheAprilETpatTampa(5.56inches)isapproximatelythatatOrlando (5.62inches).Theprocedureusedinthepreviousexamplescan be usedtoestimateactualcropETratesatlocationsthroughoutFlorida22

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wherethosecropsaregrown.Also,becausethepeakrateofwaterusebymanycropsisnearETp, andthisisoftenthecriticalperiodofwateruseintermsofeconomicyield,ETpratesaregoodindicatorsoftheratesofcropwateruseduringthosecriticalperiods.SUMMARYThe Penmanequationwasusedtocalculatemonthly,seasonal,andaveragedailypotentialevapotranspiration(ETp)ratesforninelocationsinandnearFlorida.Twentyfiveyearsofweatherrecordswereanalyzed.ProbabilitiesofoccurrenceofETprangingfromthe 50% leveltothe 95% levelweretabulated,and aprocedurewaspresentedforcalculatingotherprobabilities.TheseETpvaluescanbeusedbyirrigationmanagers,irrigationsystemdesigners,andothersinterestedinclimaticeffectson crop wateruse.They a11owcropwaterrequirementstobeestimatedatlocationsthroughoutFlorida.TheseprobabilitiesalsoallowirrigationsystemstobedesignedforaknownprobabilityofsuccessorfailureatlocationsthroughoutFlorida.REFERENCES1.Clark,G.A., andA.G.Smajstrla.1982.MethodsandEquationsUsedforPredictingPotentialEvapotranspiration.AgriculturalEngineeringDept.ResearchReport.2.Doorenbos,J.andW.O.Pruitt.1977.GuidelinesforPredictingCropWaterRequirements.Food andAgricultureOrgani-23

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zationoftheUnitedNations.Rome.144p.3.Penman,H.L.1948.fromOpenWater,ProceedingsoftheA.193:120-145.NaturalEvaporationBareSoi1andGrass.RoyalSociety,Series4.Jones,J.W.,L.H. A11en,L.C.Hammond,J.D.Martsolf,J.S.Rogers,S.F.Shih,andA.G.Smajstrla.1984.EstimatedandMeasuredEvapotranspiration for FloridaConditionsandCrops.IFASTechnicalBulletin.InPress.24

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-,.:.TI1JS.,PUblic document was promulgatedata costof$1053.48,or35.1 cents per copy,toprovide information about potential evapotranspira tion probabilities and distributionsinFlorida. 06-3M-84COOPERAT'VE EXTENS'ONSERV'CE, UN'VERS'TVOFFLOR,. DA,INSTITUTEOFFOODANDAGRICULTURALSCIENCES,K.R.Tefertliler,director,IncooperationwiththeUnitedStatesDepartment ofAgriculture, pUblls",es thisInformationtofurtherthepurposeofthe ,,"'" May8andJune30.,1914ActsofCongress;andIsauthorizedtopro-videresearch,educationalInformationandotherservicesonlytoIndi-vidualsandInstitutionsthatfunctionwithoutregardtorace,color,sexornationalorigin.SinglecopiesofExtensionpUblications(excluding4-HandYouthpublications)areavailable freetoFloridaresidentsfromCountyExtensionOffices.Informationonbulkratesorcopies fdr out-of-statepurchasersIsavailablefromC.M.Hinton,PublicatIonsDistributionCenter,IFASBuilding664,UniversityofFlorida,Gainesville,Florida32611.BeforepublicizingthispUblication,editorsshouldcontactthisaddresstodetermineavailability.



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entraJ S 'L'bc1ence'Irary APR 131987lJnive of r-, .'-,Of/daPotentialEvapotranspirationProbabilitiesAndDistributionsinFloridaI,)\ M-qrm ---I l \niP.'11.1I?lr.--J __ .:.:...llJ'_':_ltJ_1 ... BE:u:-lIe_tin_2--,o5_,,-,J),.IiA.G.Smajstrla,G.A.Clark,S.F. Shih,F.Z.Zazueta, andD.S.HarrisonFloridaCooperativeExtensionServiceInstituteofFoodandAgriculturalSCiencesUniversityofFlorida,GainesvilleJohnT.Woeste, DeanforExtensionUNIVERSITY OF FLORICAHUME LIBRARYAUG 08 198) l.F.A.S.Univ.ofFlorida

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PotentialEvapotranspirationProbabilitiesandDistributionsinFloridaA.G.Smajstrla,G.A.Clark,S.F.Shih,F.S.ZazuetaandD.S.Harrison*INTRODUCTIONPotentialevapotranspiration(ETp)istherateatwhichwater,ifavailable,wouldberemovedfromthesoilandplantsurfacesbyevaporationandtranspiration.Theconceptofpotentialevapotranspirationimpliesthattheplantcanopyisactivelygrowing andabletosupplywatertosatisfytheclimaticdemand.Energymustbeavailabletomovewatermoleculesfromtheliquidtothevaporstate.Thus,thecalculationofETpisnormallybasedatleastpartiallyonanenergybalanceattheplantcanopy.Astheavailableenergyattheplantcanopyincreases,sodoesthepotentialtoevaporatewater.ThusETpisanindexofavailableenergyattheplantcanopy.*AssociateProfessor,FormerGraduateResearchAssistant,Professor,VisitingAssistantProfessor,andProfessor,respectively,AgriculturalEngineeringDepartment,InstituteofFood andAgriculturalSciences,UniversityofFlorida,Gainesvi11e,FL32611

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Penman(1948)developedanequationtoestimatethepotentialrateofevapotranspirationfromanextensive,shortgrasscovercompletelyshadingtheground andadequatelysuppliedwithwater.ThisequationanditslatermodificationshavebeenwidelyacceptedasanindexofETp(Joneset al., 1984).Penman'sequationwasdevelopedtoestimatewaterusebyanactively-growingshort,greengrasscropwithanadequatewatersupply.ItcalculatesETpdirectlyforthoseconditionsonly.Forothercropsandconditions,Penman'sequationisonlyanindexofclimaticdemand.It can, however,beusedtocalculateactualevapotranspiration(ET)ratesforothercropsorconditionsifitiscalibratedforthoseconditions.Calibrationrequiresthedeterminationofappropriatecoefficientswhicharespecificforthecropandconditionsbeingstudied.Thesecoefficientsaretypicallycalledcropcoefficients.TheyaremultipliedbytheETptoproducetheactualcropTfortheconditionsbeingstudied.For mostcropswithcompletevegetativecanopies,cropcoefficientsaretypicallynear1.0duringpeakwateruseperiods.That is, actualETratesarenearETpratesfortheseconditions.Becausepeakwateruseratesareoftenalsothecriticalwateruseperiodsintermsofeconomicyields,ETpratesaspredictedbythePenmanequationaregoodindicatorsoftheratesofcropwateruseduringthesecriticalperiods.ETpratesascalculatedbythePenmanequationalsoprovideareferenceforthecomparisonofclimaticconditionsfromonelocationtoanother.Thus,ifactualETratesareknownforaspecificcropandculturalconditionsat2

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onelocation,ETratescanbepredictedforthesamecropandconditionsatanotherlocationifETpratesarecalculatedatbothlocations.POTENTIALETCALCULATIONSInthiswork,thePenmanequationwasusedtocalculateETpforninelocationsinandaroundFlorida,using25yearsofhourlyweatherrecords(1952-1976)obtainedfromtheNationalWeatherServiceStationsatthoselocations.TheninelocationswereMobile,Alabama(MOB),andApalachicola(APL),Tallahassee(TAL),Jacksonvi11e (JAX), Daytona Beach(DTB),0r1 and0(0 RL),Tampa(TAM),WestPalmBeach(WPB), and Miami (MIA)Florida.Thoselocationsareshowngraphicallyinfigures1-15.Hourlyweatherrecordswereaveragedortotaledtoproducedailyvalueswhich werethenusedwiththePenmanequationaspresentedbyJoneseta1.(1984)tocalculatedailyETp.Acompletedocumentationofthetechniqueused andlistsofparameterswerepresentedbyClarkandSmajstr1a(1982).DailyETpdataweresummedtoproducemonthly,seasonal,andannualvaluesinTable1.Meanand medianvaluesandtheirstandarddeviationsandcoefficientsofvariationwerealsocalculatedfortheninelocations.BothmonthlyandaveragedailyETpdataaregiven.Averagedailyvaluesweredeterminedbydividingthemonthlyvaluesbythenumberofdayspermonth.MonthlyvalueswerealsosummedtoobtainseasonalandannualETpvalues.Winterseasonalvaluesarethetota1sforthemonthsofOctoberthroughMarch.Summerseason a1valuesarethetota1sforthemonthsofAprilthroughSeptember.3

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MONTHLYETpDISTRIBUTIONSFigure16 showsmonthlyETpdistributionsfromTallahasse,Orlando,and WestPalmBeach.ThesecurvesrepresenttheextremesinETprangesinFlorida.Figure16 showsthatthepeakETpmonthinpeninsularFloridaisMay.ETpinJuneandJulyislowerthanMayduetocloudcoverwhichaccompaniesnormalsummerrainyperiods.InnorthFlorida,thepeakETpmonthistypicallyJune,althoughthereisverylittledifferenceinMay,June,andJulymonthlyETp.Table1orFigure16showsthatETprangesfromapproximately2inches/monthinDecembertoabout6inches/monthinthemonthsofMay-July.LowestannualETpvaluesoccuratTallahasseeinnorthFlorida,whereasgreatestannua1ETpvaluesoccuratWest Palm BeachinsouthFlorida.However,greatestmonthlyETpvaluesoccurinMayatOrlandoincentralFlorida.POTENTIALETPROBABILITIESMonthly,seasonal,andannualETpprobabilitiesaregiveninTable2.Probabilitieswerecalculatedbasedona normaldistributionofdata.Thisdistributionwasgraphicallydeterminedtobevalid.Anotherindicationofthenormaldistributioncanbeobservedfromthecloseagreementbetweenthemean andmedianvaluesforthemonthly,seasonal,andannualETpdatainTable1.Table2 showstheprobabilitiesthatagivenmonthlyETpwillnotbeexceeded.Forexample,the80%ETpforJanuaryis2.104

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(INCHES)JANUARYMONTHLYPOTENTIALETALADATAENTRYUPPERMIDDLELOWERPROBABILITY 50'0/080%,95%M'UI WPB.Figure1.JanuaryMonthly Potential ET.(INCHES)Figure2.FebruaryMonthlyPotential ET.WPB. lICALlIM M'U:.0'10 10"40--=-80% 95 'Y. PROBABILITY50%DATAENTRYUPPERMIDDLELOWER2.62.93.2FEBRUARYMONTHLYPOTENTIALETALA.5

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(INCHES)Figure3.MarchMonthlyPotential ET.MARCHMONTHLYPOTENTIALETWPB. teALl! I" MIUI95%50%BD"Io PROBABILITYDATAENTRYUPPER MIDDLE LOWER3.64.0 4.4ALA.(INCHES)Figure4.AprilMonthlyPotentialET.APRILMONTHLYPOTENTIALETWPB. ItAl.IItI.11." OSlO10 3040-=-=PROBABILITYDATAENTRYUPPER MIDDLE LOWER5.0 5.2 5.4 ALA.6

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(INCHES)Figure5.May Monthly Potential ET.MAYMONTHLYPOTENTIALETWP8.6.36.6eORL.7.0 !tCAUIMM'UI051010304095%80% PROBABILITY 50% DATAENTRYUPPERMIDDLELOWER5.76.06.2ALA.(INCHES)Figure6.JuneMonthly Potential ET.JUNEMONTHLYPOTENTIALETWP8. KAL!'MMIlII80%95% 50%PROBABILITYDATAENTRYUPPER MIDDLE LOWER5.86.26.5ALA.7

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(INCHES)Figure7.JulyMonthlyPotentialET.JULYMONTHLYPOTENTIALETWPB. teal......n50e;.800;. 95"I.PROBABILITY DATA ENTRYUPPERMIDDLELOWER5.55.B6.1ALA.(INCHES)KEYDATA ENTRY PROBABILITYAUGUSTMONTHLYPOTENTIALETWPB. ICAL.'"....0 80%95"1.UPPERMIDDLELOWER5.25.65.9ALA.Figure8.AugustMonthlyPotentialET.8

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(INCHES)KEYDATAENTRYPROBABILITYUPPER50%SEPTEMBERMONTHLYPOTENTIALETWPB. KALa'''.''.1U:.rV800/.950/. MIDDLELOWER4.54.85.1ALA.Figure9.SeptemberMonthlyPotential ET.(INCHES)KEYDATAENTRYPROBABILITYOCTOBERMONTHLYPOTENTIALETWPB..' ,.., 0" teALlllIIlIllLn500/. 80/.95 'Y. UPPERMIDDLELOWER3.84.1 4.4 ALA.Figure10.OctoberMonthlyPotentialET.9

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(INCHES)Figure 11. November Monthly PotentialET.NOVEMBERMONTHLYPOTENTIALETWPB. 50%80% 95%PROBABILITYDATAENTRYUPPER MIDDLE LOWER2.72.93.1ALA.(INCHES)Figure 12. December Monthly PotentialET.DECEMBERMONTtiLYPOTENTIALETWPB. ICAU... IIIIUS 40-=-=-500;.80%950/. PROBABILITYDATAENTRYUPPER MIDDLE LOWER2.0 2.2 2.4ALA.10

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APRIL-SEPTEMBER(INCHES)Figure 13.SummerPotentialET.WPB. ;n eORL35.7 50%800/.95'Y. PROBABILITYDATAENTRyUPPERMlDOLELOWERSUMMERPOTENTIALETFigure 14. Winter Potential ET.WINTERPOTENTIALETOCTOBER MARCHWPB.PROBABILITY 50'Y.80% DATAENTRY UPPER MIOOLELOWER(INCHES)ALA.11

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175Figure 15. Annual Potential ET.ANNUALPOTENTIALETWPB. ICALI11IIIIILIS50'-.eO-k95.". (INCHES)UPPERMIDDLELOWER .!5.ll DATAENTRYPROBABILITYMONTHLY ETpDISTRIBUTIONS -0TALLAHASSEE -0ORLANDO WESTPALMBEACH150 -.s::.c:0E125 ..... E ..s z0 1000:: a::Ul Z oCt 0:: tOa. oCt >W -l 50 oCti= zW tOa.25JFigure16.FMAMJJMONTHMonthly Potential ET Distribution!\, 12A SoND

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TABLE 1.STATISTICALCHARACTERISTICSOFPOTENTIALET------------------------------------------------------------------MEANDAILYMONTHLYMONTHLYETSTANDARDCOEFFICIENTMONTHETMEANETMEDIANETDEVIATIONOFVARIATION(inches)
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TABLE1(CONTINUED)STATISTICALCHARACTERISTICSOFPOTENTIALET------------------------------------------------------------------MEANDAllYMONTHLYMONTHLYfTSTANDARDCOEFFICIENTMONTHEr MEAN ET MEDIAN ETDEVIATION OF VARIATION(inches) (inches) (inches) (--) 0.15 0.15 0.13 0.06 0,060.08 0.060.09 0,09 0.10 0.09 0.10 0.30 0.38 0.470.300.330.44 0.34 0.440.42 0.360.250.210.0650.091 0.117 0.163 0.184 0.193 0.177 0.167 0.1480.1220.0890.066123 45 678 '1 101112 1'"::'LtGZI..,r-JII;rl..) MIAMI,FLORIDA10.1003.093.130.260.08 0.1313.663.700.280.0830.1574.884.940.410.0840.1915.74 5.89 0.360.06 5 0.188 5.835.83 0.560.10 6 0.176 5.29 5.420.380.0770.1835.66 5.65 0.410.0780.171 5.315.35 0.380.0790.1524.574.550.360.08100.1384.274.350.380.09110.1143.433.440.270.08120.0962.993.020.320.11-----------_._-----------------------------------------------------SUMMER0.17732.39 32.55 1.570.05WINTER0.12322.3222.231.240.06ANNUAL0.15054.71 54.95 2.430.04 .. --------------------------------Q---,{-,-:-/----,:::;.--:,-.--,-W<,0 i.. ;.,.'Il..'-",',MOBILE, 2.022.09 2.55 2.473.643.844.894.93 3.693.645.805.72 5.495.50 5.195.16 4.454.513.773.742.662.682.042.06------------------------------------------------------------------SUMMER 0.172 31.5131.531.220.04WINTER0.09216.6816.971.370.08ANNUAL 0.13248.1948.14 2.30 0.05 -----------------------------------------------------------------'1GWn '/ I \ l'( \ J' ORLANDO, FLORIIIA 10.0872.712.740.160.0620.1193.333.290.230.0730.1494.604.640.340.074 0.187 5.625.670.33 0.065 0.2036.286.380.430.076 0.1955.85 5.900.350.067 0.1895.85 5.840.250.048 0.1715.31 5.350.390.0790.1554.64 4.65 0.390.08100.1294.01 4.15 0.390.10110.1013.033.030.260.09120.0812.502.52 0.17 0.07 SUMMER 0.18333.5433.811.300.04WINTER0.11120.1820.44 0.960.05ANNUAL 0.147 53.7254.172.050.0414

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0.070.070.070.070.09 0.060.070.060.07..0.090.09 0.0:7 0.140.180.250.310.470.360.350.320.30 0.330.220.120.0640.0920.120O. 1 0.177 0.184 0.1700.1610.144 0.113 0.0790.05734 56 7 8 9101112MONTHTABLE1(CONTINUED)STATISTICALCHARACTERISTICSOFPOTENTIALET------------------------------------------_.__._---------------------MEANDAILYMONTHLYMONTHLYETSTANDARDCOEFFICIENTETMEANETMEDIANETDEVIATIONOFVARIATION (inches) (inches) (u,,:hes)(inches) (--)--..----_.-----1,0n;,--:;n---Lj""":-: ------------------TALLAHASSEE,FLORIDA1.98 1.96 2.57 2.57:l.?2 3.75 -%.75 4.715.485.37 5.53 5.555.27 5.29 4.995.014.334.313.513.522.362.361.76 1.75SUMMERWINTER ANNUAL 0.166 0.0870.12730.3415.9146.25 15.75 46.17 1.420.752.030.050.05 0.04i23 -45 67891011120.0870.117 0.1440.185 0.203 0.1960.1830.172 0.160 0.139 0.106 0.084 ;7 TAMPA,2.703.274.48 5.56 6.29 5.87 5.68 5.324.804.323.182.61 <;GN FLORIDA2.713.314.555.466.325.945.73 5.31 4.80 3.152.550.160.250.410.340.340.270.310.29 0.28 0.39 0.26 0.240.060.080.090.060.050.050.050.050.06 0.090.08 SUMMER WINTER ANNUAL0.183 0.1130.148 33.5220.56 54.09 33.4420.80 53.761.12 1.21 2.16 0.030.060.04 WESTPALMBEACH,FLORIDA10.1003.103.120.180.0620.1283.593.580.260.0730.1564.854.96 0.32 0.0740.1915.73 5.79 0.320.06 5 0.1935.98 5.85 0.520.0960.1795.375.430.340.0670.1845.725.710.370.0680.1755.435.520.270.0590.1554.654.630.390.08100.1414.364.440.500.11110.1173.523.570.270.08120.0993.073.050.280.09------------------------------------------------------------------SUMMER0.18032.8732.731.150.04WINTER0.12422.4822.551.060.05ANNUAL0.15255.3555.421.800.0315

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TABLE2.MONTHLYPOTENTIAL ET(inches) CALCULATED BY THEPENMANMETHOD. PROBABILITY LEVELATWHICHPOTENTIALETWILL NOTBE EXCEEDED.APLACHICOLA,FLORIDAMONTH12 :5 4 5(; 7 a 9 10 11 12SUMMER WINTER ANNUAL50 i.31.4 16.247.660 i.31.7 16,4 48.1 70 i. 32.016.748.580 i. 2.122.773.914.996.07 6.025.795.504.713.87 2.67 32.316.949.190 i. 2.212.884.07 J.12 6.216.17 3.945.70 4.843.972.762.07 95i. 2.282.974.215.226.336.296.075.864.954.062.832.1133.217.6 50.t. 1 '""3 45 i, 78 9101112SUMMER WItHER ANNUAL32.018.650.6[IAYTONABEACH,2.472.503.11 3.17 4.304.395.355.445.956.075.645.745.65 5.7:2 5.24 5.32 4.614.693.944.022.822.872.272.3132.519.0 51.3 FLORIDA 2.55 3.254.495.536.205.85 :'i.79 5.414.774.112.94 2.3632.719.2 51.7 33.519.852.812 34 0:"i, 78 9 101112JACKSONVILLE,2.282.323.02 3.10 4.284.365.325.396.116.276.016.135.986.095.505.614.524.613.623.682.642.702.072.11FLORIDA2.373.194.475.496.466.286.215.744.713.752.762.172.443.314.615.616.726.496.375.934.853.852.85 2.25 2.503.414.735.726.936.666.516.074.973.942.922.31SUMI1ERWINTERANNUAL32.817.650.433.217.8 51.0 33.718.051.61634.218.352.434.918.753.535.619.054.4

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TA8LE2
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TABLE2(CONTINUED)MONTHLYPOTENTIAL CALCULATEDBYTHEPENMANMETHOD.,PROBABILITYLEVELAT'WHICHPOTENTIALETWILL NOTBE EXCEEDED. MONTHSOf. 60 7. 70 7. 80 7. 90 7. TALLAHASSEE, rLORIDA 11.982.022.062.102.172.22 "' 2.57 2.62 2.662.722.802.86 033.72 3.78 3.85 3.934.04 4013 44. 4.834.91 5.01" 5.48 5.60 5.725.876.086.25. ."6 5.535.625.72 5.83 5.996.12 "7 5.275.36 5.455.56 5.725.85 a 4.995.075.165.26 5.40c-c-.,""'t,JL 9 4.33 4.4('4.48 4.58 4.714.8210 3.51 3.603.683.793.934.0511 2036 2.412.47 2.54 2.64 2.72 121. ?6 1.79 1.82 1.86 1. 911 J-------------------------------------------------------------SUMMER 30.330.7 310131.532.2 32.7WINTER15.916.116.316.516.9 17.1 ANNUAL46.346.84;'.348.0 48.949.6-------------------------------------------------------------TAMPA,FLORIDA12.702.742.782.832.90 2.96 ., 3.273.333.403.48 3.59 3.68 034.48 4.584.704.83 5.015>16 4 5.56 5.65 5.745.84 5.99 6.11::;6.296.376.466.576.726.85 5.875.946.016.106.21 6.3175.685.76 5.855.94 6.086.1985.325.395.475.56 5.69 5.7994.80 4.874.945.035.165.26 10 4.324.424.53 4.65 4.824.97113.183.253.323.403.513.61122.612.682.742.822.933.01------------------------------------------------------------SUMMER 33.533.834.134.535.0 35.4 WINTER20.620.921.2 21.6 22.122.6ANNUAL 54.1 54.655.2 55.9 56.957.6WESTPALMBEACH, FLORIDA 13.10 3.153.203.263.343.4023.59 3.65 3.723.813.924.0134.85 4.935.01'5.11 5.25 5.3745.735.815.906.006.146.26 55.98 6.11 6.25 6.426.656.8465.375.45 5.55 5.65 5.805.927 5.72 5.815.916.036.196.3385.435.505.575.665.785.879 4.65 4.744.854.975.145.28104.364.48 4.624.785.005.18113.523.593.673.75 3.873.97123.07 3014 3.213.303.42 3.52SUMMER WINTERANNUAL32.922.555.333.222.755.833.523.056.31834.323.857.734.824.258.3

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inchesatTallahassee,Florida.Thesedatashouldbeinterpretedasfollows:in8yearsoutofevery10,themonthlyETpwou1dnotbeexpectedtoexceed2.10inches.Alternatively,inonly2yearsoutof10wouldthemonthlyETpbeexpectedtobegreaterthan2.10inches.InTable2,onlytheextremelargevaluesofETparepresented.Thesewou1dbemostusefultoirrigationsystemdesignersorothersconcernedwiththemaximumexpectedratesofwaterusebycrops.ExtremesmallvaluesofETpwould beofusetowaterresourcesplannersorothersconcernedwiththehydrologicbalanceduringextremewetaswellasextremedryyears.ExtremesmallvaluesofETpcanbecalculatedfromtheextremelargevaluesandthemeanETpbecauseofthesymmetryofthenormaldistribution.Forexample,tocalculatethe 5% probability(thatis,theETpthatwouldbeexpectedtobeexceededin95yearsoutof100)annualETpforMiami,subtractthedifferencebetweenthe 95% ETpvalueandthemeanfromthemean.ForMiami,the 95% ETpis61.9inches,themeanETpis54.7inches,andthedifferenceis7.2inches.The 5% ETpisthen54.7-7.2=47.5inches.Likewise,the 10% ETpwouldbecalculatedas49.1inchesusingthe90%andthemeanETpvalues.Thisprocedurecouldbe usedformonthlyorseasonalvaluesalso.PotentialETprobabilitydataandtheir distributionsareshowngraphicallyinfigures1-15.Thefirst12figuresshowmonthlyvalues.Figures13and14showwinterand summervalues,respectively.Figure15showsthedistributionoftotalannualETpinFlorida.19

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IneachFigure,3 ETpvaluesaregivenateachlocation.Ineachcasetheuppernumberisthe50%probability(orlong-termaverage)value,themiddlenumberisthe80%probabilityvalue,andthelowernumberisthe95%probabilityvalue.GEOGRAPHICALDISTRIBUTIONSOFETpFigures1-15allowtheuserofthesedatatoobservegeographicalinfluenceson ETp.Specifically,forseasonalETp,itcanbeseenthatthereislittlevariability(approximately10%)inthesummerdatathroughoutthestate.However,considerablevariability(approximately30%)occursduringthewintermonthswhenETpvaluesremainmuchlargerinsouthFloridathaninnorthFlorida.TheuserofthesedatacanestimateETpvaluesforotherlocationsinFloridabyinterpolatingbetweenthelocationsgiveninfigures1-15.USEOFPOTENTIALETPROBABILITYDATAHistorically,manysystemswhichareinfluencedbyclimaticparametershavebeendesignedforlong-termaverageortypicalclimaticconditionsatalocation.Amongtheseareagriculturalirrigationsystems,drainagesystems,andwaterallocationsbywatermanagementdistricts.Inmanycasesthesesystemsareinfluencedmorebycertainextremeorcriticalconditionsthanlong-termaverages.Forexample,onecouldconcludethatirrigationsystemsarenotrequiredinFloridabyobservingonlylongtermaveragerainfall.Actually,short-termdroughtsarecriticaltotheeconomicproductionofmanycropsgrowninthestate.20

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Inthiscaseandinotherswhereclimaticinfluencesareimportant,theprobabilitiesofoccurrenceofextremeeventsisa moremeaningfulconsiderationforsystemdesign.InthisworktheprobabilitiesofoccurrenceofextremesinETprateshavebeencalculated.Itissuggestedthatthesevalueswillbeusefulindesignand managementofirrigationsystems.Inaddition,watermanagementdistrictsmayfindthisinformationusefulfortheprocessofallocationofwatertoagriculturalusers.Asaspecificexample,irrigationsystemscanbedesignedforaspecificgreaterprobabilityofsuccessandfailureiftheyaresizedtosupplywaterforextremeuseconditionsratherthanonlyaverageconditions.ACTUALEVAPOTRANSPIRATIONInthiswork,onlypotentialevapotranspirationrateshavebeencalculated.ActualevapotranspirationforagivencropmaybegreaterorlessthanETpdependinguponthenatureofthecropanditsstageofgrowth.Todeterminetheactualevapotranspirationforaspecificcrop,ETpmustbemutlipliedby acropcoefficientwhichdependsuponthecropspeciesandstageofgrowth.Cropcoefficientsformanycropswerepresentedby andPruitt(1977). CropcoefficientsexpresstherelationshipbetweentheactualETandETpforaspecificcrop.TheplantsurfacereferencedbyETpdependsuponhowtheETpequationwasoriginallyderivedandcalibrated.ForthePenmanequationtheplantsurfacereferencedisthepreviously-discussedshortgrasscover.Thus,forthePenmanequa-21

PAGE 23

tion,cropcoefficientsexpresstheratiooftherelativewateruseofaspecificcropandconditionstothatofawell-wateredshortgreengrassreferencecrop.AlthoughactualETratesforspecificcropsarenotpresentedinthispublication,potentialevapotranspirationratesascalculatedbythePenmantechniqueareusefulbecausetheyprovideareferenceforthecomparisonofclimaticconditionsfrom onelocationtoanother.Forexample,iftheactualETduringAprilforacropgrownnearOrlandowas4.0inches,theactualETcanbecalculatedforthesamecropnearTallahasseeusingtheETpdatainthispublication.FirstcalculatetheratioofETpatthenewlocation(Tallahassee,inthisexample)totheETpatthelocationwhereactualcropETisknown(Orlando).Inthisexamp 1e,theApri 1ETpforTa11ahaseeis4.75inches(Table1),andtheAprilETpforOrlandois5.62inches.Theratioisthen4.75/5.62=0.845.Toca1 cu 1atetheestimatedactualETatTallahassee,multiplytheactualETknownforOrlandobythepreviouslycalculatedratio.Inthisexample,(4.0inches)(0.845)=3.38incheswouldbetheestimatedactualETforAprilatTallahassee.Asanotherexample,iftheactualETduringAprilforthepreviously-discussedcropnearOrlandowas4.0inches,thentheactualETnearTampawillalsobeapproximately4.0inches.Thiscan bedeterminedbyobservingthattheAprilETpatTampa(5.56inches)isapproximatelythatatOrlando (5.62inches).Theprocedureusedinthepreviousexamplescan be usedtoestimateactualcropETratesatlocationsthroughoutFlorida22

PAGE 24

wherethosecropsaregrown.Also,becausethepeakrateofwaterusebymanycropsisnearETp, andthisisoftenthecriticalperiodofwateruseintermsofeconomicyield,ETpratesaregoodindicatorsoftheratesofcropwateruseduringthosecriticalperiods.SUMMARYThe Penmanequationwasusedtocalculatemonthly,seasonal,andaveragedailypotentialevapotranspiration(ETp)ratesforninelocationsinandnearFlorida.Twentyfiveyearsofweatherrecordswereanalyzed.ProbabilitiesofoccurrenceofETprangingfromthe 50% leveltothe 95% levelweretabulated,and aprocedurewaspresentedforcalculatingotherprobabilities.TheseETpvaluescanbeusedbyirrigationmanagers,irrigationsystemdesigners,andothersinterestedinclimaticeffectson crop wateruse.They a11owcropwaterrequirementstobeestimatedatlocationsthroughoutFlorida.TheseprobabilitiesalsoallowirrigationsystemstobedesignedforaknownprobabilityofsuccessorfailureatlocationsthroughoutFlorida.REFERENCES1.Clark,G.A., andA.G.Smajstrla.1982.MethodsandEquationsUsedforPredictingPotentialEvapotranspiration.AgriculturalEngineeringDept.ResearchReport.2.Doorenbos,J.andW.O.Pruitt.1977.GuidelinesforPredictingCropWaterRequirements.Food andAgricultureOrgani-23

PAGE 25

zationoftheUnitedNations.Rome.144p.3.Penman,H.L.1948.fromOpenWater,ProceedingsoftheA.193:120-145.NaturalEvaporationBareSoi1andGrass.RoyalSociety,Series4.Jones,J.W.,L.H. A11en,L.C.Hammond,J.D.Martsolf,J.S.Rogers,S.F.Shih,andA.G.Smajstrla.1984.EstimatedandMeasuredEvapotranspiration for FloridaConditionsandCrops.IFASTechnicalBulletin.InPress.24

PAGE 26

-,.:.TI1JS.,PUblic document was promulgatedata costof$1053.48,or35.1 cents per copy,toprovide information about potential evapotranspira tion probabilities and distributionsinFlorida. 06-3M-84COOPERAT'VE EXTENS'ONSERV'CE, UN'VERS'TVOFFLOR,. DA,INSTITUTEOFFOODANDAGRICULTURALSCIENCES,K.R.Tefertliler,director,IncooperationwiththeUnitedStatesDepartment ofAgriculture, pUblls",es thisInformationtofurtherthepurposeofthe ,,"'" May8andJune30.,1914ActsofCongress;andIsauthorizedtopro-videresearch,educationalInformationandotherservicesonlytoIndi-vidualsandInstitutionsthatfunctionwithoutregardtorace,color,sexornationalorigin.SinglecopiesofExtensionpUblications(excluding4-HandYouthpublications)areavailable freetoFloridaresidentsfromCountyExtensionOffices.Informationonbulkratesorcopies fdr out-of-statepurchasersIsavailablefromC.M.Hinton,PublicatIonsDistributionCenter,IFASBuilding664,UniversityofFlorida,Gainesville,Florida32611.BeforepublicizingthispUblication,editorsshouldcontactthisaddresstodetermineavailability.


Potential evapotranspiration probabilities and distributions in Florida
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Permanent Link: http://ufdc.ufl.edu/UF00008551/00001
 Material Information
Title: Potential evapotranspiration probabilities and distributions in Florida
Series Title: Bulletin Florida Cooperative Extension Service
Physical Description: 24 p. : ill., maps ; 23 cm.
Language: English
Creator: Smajstrla, A. G ( Allen George )
Publisher: Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of FLorida
Place of Publication: Gainesville
Publication Date: 1984?
 Subjects
Subjects / Keywords: Evapotranspiration -- Florida   ( lcsh )
Genre: bibliography   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
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 Notes
Abstract: The Penman equation was used to calculate monthly and average daily potential evapotranspiration (ETp) rates for nine locations in and near Florida. Twenty-five years of weather records were analyzed. Probabilities of occurrence of ETp ranging from the 50% level to the 95% level were tabulated, and a procedure was presented for calculating other probabilities. These ETp values will be useful to irrigation managers, irrigation system designers, and others. interested in climatic effects on crop water use. These probabilities allow irrigation systems to be designed for a known probability of success or failure at locations throughout Florida. They also allow crop water requirements to be estimated at locations through Florida.
Bibliography: Includes bibliographical references (p. 23-24).
General Note: Cover title.
Statement of Responsibility: A.G. Smajstrla ... et al..
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Full Text

Bulletin 205


Potential Evapotranspiration Probabilities

And Distributions in Florida


A. G. Smajstria, G. A. Clark, S. F. Shih, F. Z. Zazueta, and D. S. Harrison
Florida Cooperative Extension Service
Institute of Food and Agricultural Sciences
University of Florida, Gainesville
John T. Woeste, Dean for Extension


HUME LIBRARY

AUG 0 8 19'

I.F.A.S.- Univ. of Florida


I -.T- -. -1--.-A I












Potential Evapotranspiration Probabilities
and Distributions in Florida

A.G. Smajstrla, G.A. Clark, S.F. Shih,
F.S. Zazueta and D.S. Harrison*


INTRODUCTION

Potential evapotranspiration (ETp) is
the rate at which water, if available,
would be removed from the soil and plant
surfaces by evaporation and transpiration.
The concept of potential evapotranspiration
implies that the plant canopy is actively
growing and able to supply water to satisfy
the climatic demand.
Energy must be available to move water
molecules from the liquid to the vapor state.
Thus, the calculation of ETp is normally
based at least partially on an energy balance
at the plant canopy. As the available
energy at the plant canopy increases, so
does the potential to evaporate water. Thus
ETp is an index of available energy at the
plant canopy.


* Associate Professor, Former Graduate
Research Assistant, Professor, Visiting
Assistant Professor, and Professor,
respectively, Agricultural Engineering
Department, Institute of Food and Agricultural
Sciences, University of Florida,
Gainesville, FL 32611









Penman (1948) developed an equation to
estimate the potential rate of evapotranspira-
tion from an extensive, short grass cover
completely shading the ground and adequately
supplied with water. This equation and its
later modifications have been widely accepted
as an index of ETp (Jones et al., 1984).

Penman's equation was developed to
estimate water use by an actively-growing
short, green grass crop with an adequate water
supply. It calculates ETp directly for those
conditions only. For other crops and
conditions, Penman's equation is only an
index of climatic demand. It can, however, be
used to calculate actual evapotranspiration
(ET) rates for other crops or conditions if
it is calibrated for those conditions.
Calibration requires the determination of
appropriate coefficients which are specific
for the crop and conditions being studied.
These coefficients are typically called crop
coefficients. They are multiplied by the ETp
to produce the actual crop ET for the
conditions being studied.

For most crops with complete vegetative
canopies, crop coefficients are typically
near 1.0 during peak water use periods.
That is, actual ET rates are near ETp rates
for these conditions. Because peak water
use rates are often also the critical water
use periods in terms of economic yields, ETp
rates as predicted by the Penman equation are
good indicators of the rates of crop water
use during these critical periods. ETp rates
as calculated by the Penman equation also
provide a reference for the comparison of
climatic conditions from one location to
another. Thus, if actual ET rates are known
for a specific crop and cultural conditions at









one location, ET rates can be predicted
for the same crop and conditions at another
location if ETp rates are calculated at both
locations.

POTENTIAL ET CALCULATIONS

In this work, the Penman equation was
used to calculate ETp for nine locations in
and around Florida, using 25 years of hourly
weather records (1952-1976) obtained from
the National Weather Service Stations at
those locations. The nine locations were
Mobile, Alabama (MOB), and Apalachicola (APL),
Tallahassee (TAL), Jacksonville (JAX), Daytona
Beach (DTB), Orlando (ORL), Tampa (TAM), West
Palm Beach (WPB), and Miami (MIA) Florida.
Those locations are shown graphically in
figures 1-15.

Hourly weather records were averaged or
totaled to produce daily values which were
then used with the Penman equation as
presented by Jones et al. (1984) to calculate
daily ETp. A complete documentation of the
technique used and lists of parameters were
presented by Clark and Smajstrla (1982).

Daily ETp data were summed to produce
monthly, seasonal, and annual values in Table
1. Mean and median values and their standard
deviations and coefficients of variation were
also calculated for the nine locations. Both
monthly and average daily ETp data are
given. Average daily values were determined
by dividing the monthly values by the number
of days per month. Monthly values were also
summed to obtain seasonal and annual ETp
values. Winter seasonal values are the
totals for the months of October through
March. Summer seasonal values are the totals
for the months of April through September.









MONTHLY ETp DISTRIBUTIONS


Figure 16 shows monthly ETp distributions
from Tallahasse, Orlando, and West Palm
Beach. These curves represent the extremes
in ETp ranges in Florida. Figure 16 shows
that the peak ETp month in peninsular
Florida is May. ETp in June and July is
lower than May due to cloud cover which
accompanies normal summer rainy periods.
In north Florida, the peak ETp month is
typically June, although there is very little
difference in May, June, and July monthly ETp.

Table 1 or Figure 16 shows that ETp
ranges from approximately 2 inches/month in
December to about 6 inches/month in the months
of May July. Lowest annual ETp values
occur at Tallahassee in north Florida,
whereas greatest annual ETp values occur at
West Palm Beach in south Florida. However,
greatest monthly ETp values occur in May at
Orlando in central Florida.

POTENTIAL ET PROBABILITIES

Monthly, seasonal, and annual ETp
probabilities are given in Table 2.
Probabilities were calculated based on a
normal distribution of data. This distribution
was graphically determined to be valid.
Another indication of the normal distribution
can be observed from the close agreement
between the mean and median values for the
monthly, seasonal, and annual ETp data in
Table 1.

Table 2 shows the probabilities that a
given monthly ETp will not be exceeded. For
example, the 80% ETp for January is 2.10













ALA


JANUARY

MONTHLY POTENTIAL ET

(INCHES)


Figure 1. January Monthly Potential ET.



ALA.


2.9 2.9
3.2 MOB. '


FEBRUARY

MONTHLY POTENTIAL ET

(INCHES)









KEY

DATA ENTRY PROBABILITY
UPPER 50%
MIDDLE 80%
LOWER 95 %


Figure 2. February Monthly Potential ET.


ecgsamW_


MALE M ME.S3
0>lp r0 4


.,tft











ALA.
3.7
3.6 3.9
4.0 3.6 4.1
4.4 MOB. 3.9
4.2

'APL




MARCH

MONTHLY POTENTIAL ET


(INCHES)










KEY

DATA ENTRY PROBABILITY
UPPER 50%
MIDDLE 80%
LOWER 95%



Figure 3. March Monthly Potential ET.





ALA.
4.8
5.0 5.00
5.2 4.7 5.3
5.4 MOB 50 /


APRIL

MONTHLY POTENTIAL ET


(INCHES)


Figure 4. April Monthly Potential ET.


ec ls mas
our a M1LE
olal0 ^


*CurE MNMue
roatolo























MAY

MONTHLY POTENTIAL ET

(INCHES)









KEY

DATA ENTRY PROBABILITY
UPPER 50%
MIDDLE 80%
LOWER 95%


Figure 5. May Monthly Potential ET.


ALA.

5.8
6.2
6.5 MOB


JUNE

MONTHLY POTENTIAL ET

(INCHES)









KEY

DATA ENTRY PROBABILITY
UPPER 50%
MIDOOLE 80%
LOWER 95%


Figure 6. June Monthly Potential ET.


KALE I MILE
smemo uo


.,Lop* -


SCLE IM amas
o 8 10 4











ALA.
5.3
5.5 5.6 T
5.8 55 5.9
6.1 MOB. 55
6.1
Ct-
APL




JULY

MONTHLY POTENTIAL ET

(INCHES)









KEY

DATA ENTRY PROBABILITY
UPPER 50%
MIDDLE 80%
LOWER 95 %



Figure 7. July Monthly Potential ET.




ALA D
5.0
5.2 5.3g
5.6 5.5
5.1
5.9 MOB 5.5
5.9
C-~





AUGUST

MONTHLY POTENTIAL ET

(INCHES)









KEY

DATA ENTRY PROBABILITY
UPPER 50%
MIDDLE 80%
LOWER 95%



Figure 8. August Monthly Potential ET.


o, "" -o


o 1toao4


c*C sms
o01^0^a











ALA.

4.5
4.8
5.1 MOB.


SEPTEMBER

MONTHLY POTENTIAL ET

(INCHES)









KEY

DATA ENTRY PROBABILITY
UPPER 50%
MIDDLE 80%
LOWER 95%



Figure 9. September Monthly Potential ET.


OCTOBER

MONTHLY POTENTIAL ET

(INCHES)









KEY

DATA ENTRY PROBABILITY
UPPER 50%
MIDDLE 80%
LOWER 95%



Figure 10. October Monthly Potential ET.


UNE I00 M40C
KT=


.^-~


sCUE m ME
o aU










ALA
ALA. JAX.
2.9
2.7 2.5 TAL. 2 V
2.9 2.7 2.9
3.1 MOB 2.5
2.7

A 2.L.
2.9 DTB
3.1


NOVEMBER 3.0
(3.2 3.3 g ORL.
MONTHLY POTENTIAL ET 33.
TAM.
(INCHES)





3 .5 WPB


KEY
DATA ENTRY PROBABILITY 3 MIA.
UPPER 50%
MIDDLE 80% wsore ^
LOWER 95%


Figure 11. November Monthly Potential ET.






ALA.


2.4 MOB. 1.9


DECEMBER

MONTHLY POTENTIAL ET

(INCHES)


Figure 12. December Monthly Potential ET.

10


ru m MtES











ALA. --
30.3
31.0 31.5 0
32.s5--- 32.7
33.5 MOB 31.4
32.3
3.2 -

-APL.




APRIL SEPTEMBER

SUMMER POTENTIAL ET


(INCHES)










KEY

DATA ENTRY PROBABILITY
UPPER 50%
MIDDLE 80%
LOWER 95 %



Figure 13. Summer Potential ET.


OCTOBER MARCH

WINTER POTENTIAL ET


(INCHES)


KEY

DATA ENTRY PROBABILITY
UPPER 50%
MIDDLE 80 %
LOWER 95 %


Figure 14. Winter Potential ET.


senl m M s
i^o ao 4


ecL m Mle
0o 40








ALA. (
48. -- 48. 4 0 TAL 54 4
50.1 1.6 49.6
52.0 MOB. 49.1
50.6





54.1 55
ANNUAL POTENTIAL ET '9 "
S57.6
TAM.
(INCHES)











Figure 15. Annual Potential ET.

175









E
MONTHLY ETp DISTRIBUTE








E 1250
1 //


E
z

_o

too
a- /
z

7! -
O
a.

w

50(- --- TALLAHASSEE

z -El- ORLANDO

.-A-- WEST PALM BEACH
0.
25 1 I 1 1 I ( 1
J F M A M J J A S
MONTH
Figure 16. Monthly Potential ET Distributions.
12


)NS


0 N D











TABLE 1.
STATISTICAL CHARACTERISTICS OF POTENTIAL ET
MEAN DAILY MONTHLY MONTHLY ET STANDARD COEFFICIENT
MONTH ET MEAN ET MEDIAN ET DEVIATION OF VARIATION
(inches) (inches) (inches) (inches) (--)

--- APLACHICOLA, FLORIDA ---
1 0.063 1.95 1.96 0.20 0.10
2 0.092 2.57 2.61 0.24 0.09
3 0.116 3.61 3.56 0.36 0.10
4 0.158 4.74 4.72 0.29 0.06
5 0.187 5.80 5.79 0.33 0.06
6 0.191 5.74 5.82 0.34 0.06
7 0.178 5.51 5.45 0.34 0.06
8 0.165 5.12 5.20 0.45 0.09
Y 0.149 4.46 4,50 0.29 0.07
10 0.118 3.67 3.74 0.24 0.06
11 0.084 2.51 2.50 0.19 0.08
12 0.062 1.92 1.93 0.12 0.06
SUMMER 0.171 31.37 31.46 1.10 0.04
WINTER 0.089 16.22 16.29 0.82 0.05
ANNUAL 0.130 47.59 48.26 1.82 0.04
-------------------------------------- *-______---------------__ __

--- DAYTONA BEACH, FLORIDA ---
1 0.078 2.43 2.41 0.14 0.06
2 0.109 3.05 3.03 0.23 0.08
3 0.136 4.22 4.37 0.31 0.07
4 0.176 5.27 5.24 0.31 0.06
5 0.189 5.84 5.89 0.43 0.07
6 0.185 5.55 5.53 0.35 0.06
7 0.180 5.59 5.62 0.24 0.04
8 0.167 5.17 5.13 0.28 0.05
9 0 151 4.54 4.61 0.28 0.06
10 0.125 3.87 3.89 0.28 0.07
11 0.092 2.77 2.75 0.20 0.07
12 0.072 2.23 2.23 0.15 0.07
SUMMER 0.175 31.98 32.03 0.91 0.03
WINTER 0.102 18.58 18.77 0.76 0.04
ANNUAL 0.139 50.56 50.51 1.37 0.03
..------------------------------------------


0.072
0.105
0.135
0.175
0.192
0.196
0.190
0.174
0.148
0.115
0.087
0.065


--- JACKSONVILLE,
2.24
2.95
4.19
5.24
5.96
5.89
5.89
5.40
4.43
3.56
2.60
2.02


FLORIDA
2.24
2.93
4.13
5.19
6.05
5.83
5.90
5.41
4.46
3.56
2.61
1.98


0.16
0.28
0.32
0.29
0.59
0.47
0.38
0.41
0.33
0.23
0.20
0.17


0.07
0.09
0.08
0 06
0.10
0.08
0.06
0.08
0.07
0.07
0.08
0.09


SUMMER 0.179 32.81 33.10 1.67 0.05
WINTER 0.097 17.56 17.47 0.87 0.05
ANNUAL 0.138 50.37 50.60 2.42 0.05











TABLE 1 (CONTINUED)
STATISTICAL CHARACTERISTICS OF POTENTIAL ET
------------..----------------------------------------------------
MEAN DAILY MONTHLY MONTHLY ET STANDARD COEFFICIENT
MONTH Er MEAN ET MEDIAN ET DEVIATION OF VARIATION
(inches) (inches) (inches) (inches) (--)
~ l -? :' ; i.rJ ( o i I / '-
MIAMI. FLORIDA ---
1 0.100 3.09 3.13 0.26 0.08
2 0.131 3.66 3.70 0.28 0.08
3 0.157 4.88 4.94 0.41 0.08
4 0.191 5.74 5.89 0.36 0.06
5 0.188 5.83 5.83 0.56 0.10
6 0.176 5.29 5.42 0.38 0.07
7 0.183 5.66 5.65 0.41 0.07
8 0.171 5.31 5.35 0.38 0.07
9 0.152 4.57 4.55 0.36 0.08
10 0.138 4,27 4.35 0.38 0.09
11 0.114 3.43 3.44 0.27 0.08
12 0.096 2.99 3.02 0.32 0.11
...................................................................
SUMMER 0.177 32.39 32.55 1.57 0.05
WINTER 0.123 22.32 22.23 1,24 0.06
ANNUAL 0.150 54.71 54.95 2.43 0.04
.----------------.-.-....... ......................--- -.-.. .......

MOBILE. ALABAMA --
1 0.065 2.02 2.09 0.30 0.15
2 0.091 2.55 2.47 0.38 0.15
3 0.117 3.64 3.84 0.47 0.13
4 0.163 4.89 4.93 0.30 0.06
5 0.184 5.69 5.64 0.33 0.06
6 0.193 5.80 5.72 0.44 0.08
7 0.177 5.49 5.50 0.34 0.06
8 0.167 5.19 5.16 0.44 0.09
9 0.148 4.45 4.51 0.42 0.09
10 0.122 3.77 3.74 0.36 0.10
11 0.089 2.66 2.68 0.25 0.09
12 0.066 2.04 2.06 0.21 0.10

SUMMER 0.172 31.51 31.53 1.22 0.04
WINTER 0.092 16.68 16.97 1.37 0.08
ANNUAL 0.132 48.19 48.14 2.30 0.05
..................................................................

ORLANDO, FLORIDA ---
1 0.087 2.71 2.74 0.16 0.06
2 0.119 3.33 3.29 0.23 0.07
3 0.149 4.60 4.64 0.34 0.07
4 0.187 5.62 5.67 0.33 0.06
5 0,203 6.28 6.38 0.43 0.07
6 0.195 5.85 5.90 0.35 0.06
7 0.189 5.85 5.84 0.25 0.04
8 0.171 5.31 5.35 0.39 0.07
9 0.155 4.64 4.65 0.39 0.08
10 0.129 4,01 4.15 0.39 0.10
11 0.101 3.03 3.03 0.26 0.09
12 0.081 2.50 2.52 0.17 0,07
UMMER-- --,-----------------------------------------------------
SUMMER 0,183 33.54 33.81 1.30 0.04
WINTER 0.111 20.18 20.44 0.96. 0.05
ANNUAL 0.147 53.72 54.17 2.05 0.04
..................................................................











TABLE 1 (CONTINUED)
STATISTICAL CHARACTERISTICS OF POTENTIAL ET
........................................................................
MEAN DAILY MONTHLY MONTHLY ET STANDARD COEFFICIENT
MONTH ET MEAN ET MEDIAN ET DEVIATION OF VARIATION
(inches) (inches) ((ic hes) (inches) (--)es
...........-- ------------------ ------------------

--- TALLAHASSEE. FLORIDA ---
1 0.064 1.98 1.96 0.14 0.07
2 0.092 2.57 2.57 0.18 0.07
3 0.120 3.72 3.75 0.25 0.07
4 0.158 4.75 4.71 0.31 0.07
5 0.177 5.48 5.37 0.47 0.09
6 0.184 5.53 5.55 0.36 0.06
7 0.170 5.27 5.29 0.35 0.07
8 0.161 4.99 5.01 0.32 0.06
9 0.144 4.33 4.31 0.30 0.07
10 0.113 3.51 3.52 0.33 0.09
11 0.079 2.36 2.36 0.22 0.09
12 0.057 1.76 1.75 0.12 0.07
SUMMER 0.166 30.34 30.12 1.42 0.05
WINTER 0.087 15.91 15.75 0.75 0.05
ANNUAL 0.127 46.25 46.17 2.03 0.04
------------------------------------_--------_-_^^_---_---_
W? 4U sotJ 5 %o 7 ? 1) -
--- TAMPA, FLORIDA ---
1 0.087 2.70 2.71 0.16 0.06
2 0.117 3.27 3.31 0.25 0.08
3 0.144 4.48 4.55 0.41 0.09
4 0.185 5.56 5.46 0.34 0.06
5 0.203 6.29 6.32 0.34 0.05
6 0.196 5.87 5.94 0.27 0.05
7 0.183 5.68 5.73 0.31 0.05
8 0.172 5.32 5.31 0.29 0.05
9 0.160 4.80 4.80 0.28 0.06
10 0.139 4.32 4.31 0.39 0.09
11 0.106 3.18 3.15 0.26 0.08
12 0.084 2.61 2.55 0.24 0.09
SUMMER 0.183 33.52 33.44 1.12 0.03
WINTER 0.113 20.56 20.80 1.21 0.06
ANNUAL 0.148 54.09 53.76 2.16 0.04
-------------------------.-- -----------^.-----------------

--- WEST PALM BEACH, FLORIDA ---
1 0.100 3.10 3.12 0.18 0.06
2 0.128 3.59 3.58 0.26 0.07
3 0.156 4.85 4.96 0.32 0.07
4 0.191 5.73 5.79 0.32 0.06
5 0.193 5.98 5.85 0.52 0.09
6 0.179 5.37 5.43 0.34 0.06
7 0.184 5.72 5.71 0.37 0.06
8 0.175 5.43 5.52 0.27 0.05
9 0.155 4.65 4.63 0.39 0.08
10 0.141 4.36 4.44 0.50 0.11
11 0.117 3.52 3.57 0.27 0.08
12 0.099 3.07 3.05 0.28 0.09
-----------------------------------------------------..-----------
SUMMER 0.180 32.87 32.73 1.15 0.04
WINTER 0.124 22.48 22.55 1.06 0.05
ANNUAL 0.152 55.35 55.42 1.80 0.03











TABLE 2.
MONTHLY POTENTIAL ET(inches) CALCULATED BY THE PENMAN METHOD.
PROBABILITY LEVEL AT WHICH POTENTIAL ET WILL NOT BE EXCEEDED.

MONTH 50 % 60 % 70 % 80 % 90 % 95 %

--- APLACHICOLA, FLORIDA ---
1 1.95 2.00 2.06 2.12 2.21 2.28
2 2.57 2.63 2.70 2.77 2.88 2.97
3 3.61 3.70 3.80 3.91 4.07 4.21
4 4.74 4.82 4.90 4.99 5.12 5.22
5 5.80 5.88 5.97 6.07 6.21 6.33
6 5.74 5.83 5.92 6.02 6.17 6.29
7 5.51 5.59 5.69 5.79 5.94 6.07
8 5.12 5.24 5.36 5,50 5.70 5.86
9 4.46 4.54 4.62 4.71 4.84 4.95
10 3.67 3.73 3.79 3.87 3.97 4.06
11 2.51 2.56 2.61 2.67 2.76 2.83
12 1,92 1.95 1.98 2.02 2.07 2.11
.............................................................
SUMMER 31.4 31.7 32.0 32.3 32.8 33.2
WINTER 16.2 16.4 16.7 16.9 17.3 17.6
ANNUAL 47.6 48.1 48.5 49.1 49.9 50.6

--- DAYTONA BEACH, FLORIDA ---
1 2.43 2.47 2.50 2.55 2.61 2.06
2 3.05 3,11 3.17 3.25 3.35 3.43
3 4.22 4.30 4.39 4.49 4.62 4.73
4 5,27 5.35 5.44 5,53 5.67 5.78
5 5.84 5.95 6.07 6.20 6.39 6,55
6 5.55 5.64 5.74 5.85 6.01 6.13
7 5.59 5.65 5.72 5,79 5.90 5.98
8 5.17 5.24 5.32 5.41 5.53 5.63
9 4.54 4.61 4.69 4.77 4.90 5.00
10 3.87 3,94 4.02 4.11 4.23 4.34
11 2.77 2.82 2.87 2.94 3.02 3.10
12 2.23 2.27 2.31 2.36 2.42 2.48
..............................................................
SUMMER 32.0 32.2 32.5 32.7 33.1 33.5
WINTER 18.6 18.8 19.0 19.2 19.5 19.8
ANNUAL 50.6 50.9 51.3 51.7 52.3 52.8
--------..----------------------------------------------------

--- JACKSONVILLE, FLORIDA ---
1 2.24 2.28 2.32 2.37 2.44 2.50
2 2.95 3.02 3.10 3.19 3.31 3.41
3 4.19 4.28 4.36 4.47 4.61 4.73
4 5.24 5.32 5.39 5.49 5.61 5.72
5 5.96 6.11 6.27 6.46 6.72 6.93
6 5.89 6.01 6.13 6.28 6.49 6.66
7 5.89 5.98 6.09 6.21 6.37 6.51
8 5.40 5.50 5.61 5.74 5.93 6.07
9 4.43 4.52 4.61 4.71 4.85 4.97
10 3.56 3.62 3.68 3.75 3.85 3.94
11 2.60 2.64 2.70 2.76 2.85 2.92
12 2.02 2.07 2.11 2.17 2.25 2.31
SUMMER 32.8 33.2 33.7 34.2 34.9 35.6
WINTER 17.6 17.8 18.0 18.3 18.7 19.0
ANNUAL 50.4 51.0 51.6 52.4 53.5 54.4








16
16











TABLE 2 (CONTINUED)
MONTHLY POTENTIAL ET(inches) CALCULATED BY THE PENMAN METHOD.
PROBABILITY LEVEL AT WHICH POTENTIAL ET WILL NOT BE EXCEEDED.

MONTH 50 7 60 % 70 X 80 X 90 % 95 %

--- MIAMI, FLORIDA ---
1 3.09 3.16 3.23 3.31 3.42 3.51
2 3.66 3.73 3.81 3.90 4.02 4.13
3 4.88 4.98 5.09 5.23 5.41 5,56
4 5,74 5.83 5.93 6.04 6.20 6.33
5 5.83 5.97 6.12 6.30 6.55 6.75
6 5.29 5.38 5.49 5.61 5.78 5.92
7 5.66 5,76 5.88 6.01 6.19 6,33
8 5,31 5.40 5.51 5.63 5.79 5.93
9 4.57 4.66 4.76 4.87 5.03 5.16
10 4.27 4.37 4.47 4.59 4.76 4.90
11 3.43 3.50 3.57 3.65 3.77 3.87
12 2.99 3.07 3.15 3.25 3.39 3.51
SUMMER 32.4 32.8 33.2 33.7 34,4 35.0
WINTER 22.3 22.6 23.0 23.4 23.9 24.4
ANNUAL 54.7 55.3 56,0 56.8 57.8 58.7

--- MOBILE, ALABAMA --
1 2.02 2.09 2.17 2.27 2.40 2.51
2 2.55 2.64 2.75 2.87 3.04 3.18
3 3.64 3.76 3.89 4.04 4.25 4.42
4 4.89 4,97 5.05 5.15 5.28 5.39
5 5,69 5.77 5.87 5.97 6.12 6,24
6 5.80 5.91 6.03 6.16 6.36 6.5.1
7 5.49 5.58 5.67 5.78 5.92 6.05
8 5.19 5.30 5.42 5.56 5.76 5.92
9 4.45 4.55 4.67 4.80 4.98 5.14
10 3.77 3.86 3.96 4.08 4.23 4.37
11 2.66 2.73 2.79 2.87 2.98 3.07
12 2.04 2.10 2.16 2.22 2.32 2.40
SUMMER 31.5 31.8 32.2 32,5 33.1 33,5
WINTER 16.7 17.0 17.4 17.8 18.4 18.9
ANNUAL 48.2 48.8 49.4 50.1 51.1 52.0

--- ORLANDO, FLORIDA ---
1 2.71 2.75 2.80 2.85 2.92 2.98
2 3.33 3.39 3.45 3.53 3.63 3.71
3 4.60 4.69 4.78 4.89 5.04 5.16
4 5.62 5.70 5.79 5.90 6.05 6.17
5 6.28 6.39 6.50 6.64 6.83 6.99
6 5.85 5.94 6.04 6.15 6.30 6.43
7 5,85 5.91 5.98 6.05 6.16 6.25
8 5.31 5.40 5.51 5.64 5.81 5.95
9 4.64 4.73 4.84 4.96 5.13 5,27
10 4.01 4.11 4.21 4.33 4.51 4.65
11 3.03 3.09 3.16 3.25 3.36 3.46
12 2.50 2.54 2.59 2.64 2.72 2.78
SUMMER 33.5 33.9 34.2 34.6 35.2 35.7
WINTER 20.2 20.4 20.7 21.0 21.4 21.8
ANNUAL 53.7 54.2 54.8 55.4 56.3 57.1










TABLE 2 (CONTINUED)
MONTHLY POTENTIAL ET(inches) CALCULATED BY THE PENMAN METHOD.
PROBABILITY LEVEL AT WHICH POTENTIAL ET WILL NOT BE EXCEEDED.

MONTH 50 % 60 % 70 % 80 % 90 % 95 %

--- TALLAHASSEE, FLORIDA ---
1 1.98 2.02 2.06 2.10 2.17 2.22
2 2.57 2.62 2.66 2.72 2.80 2.86
3 3.72 3.78 3.85 3.93 4.04 4.13
4 4.75 4.83 4.91 5.01 5.15 5.26
5.48 5.60 5.72 5.87 6.08 6.25
6 5.53 5.62 5.72 5.83 5.99 6.12
7 5.27 5.36 5.45 5.56 5.72 5.85
8 4.99 5.07 5.16 5.26 5.40 5.52
9 4.33 4.40 4.48 4.58 4.71 4.82
10 3.51 3.60 3,68 3.79 3.93 4.05
11 2.36 2.41 2.47 2.54 2.64 2.72
12 1.76 1.79 1.82 1.86 1.91 1.95
/ SUMMER 30.3 30.7 31.1 31.5 32.2 32.7
WINTER 15.9 16.1 16.3 16.5 16.9 17.1
ANNUAL 46.3 46.8 47.3 48.0 48.9 49.6

--- TAMPA, FLORIDA ---
1 2.70 2.74 2.78 2.83 2.90 2.96
2 3.27 3.33 3.40 3.48 3.59 3.68
3 4.48 4.58 4.70 4.83 5.01 5.16
4 5.56 5.65 5.74 5.84 5.99 6.11
5 6.29 6.37 6.46 6.57 6.72 6.85
6 5.87 5.94 6.01 6.10 6.21 6.31
7 5.68 5.76 5.85 5.94 6.08 6.19
8 5.32 5.39 5.47 5.56 5.69 5.79
9 4.80 4.87 4.94 5.03 5.16 5.26
10 4.32 4.42 4.53 4.65 4.82 4.97
11 3.18 3.25 3.32 3.40 3.51 3.61
12 2.61 2.68 2.74 2.82 2.93 3.01
SUMMER 33.5 33.8 34.1 34.5 35.0 35.4
WINTER 20.6 20.9 21.2 21.6 22.1 22.6
ANNUAL 54.1 54.6 55.2 55.9 56.9 57.6

--- WEST PALM BEACH, FLORIDA ---
1 3.10 3.15 3.20 3.26 3.34 3.40
2 3.59 3.65 3.72 3.81 3.92 4.01
3 4.85 4.93 5.01 '5.11 5.25 5.37
4 5.73 5.81 5.90 6.00 6.14 6.26
5 5.98 6.11 6.25 6.42 6.65 6.84
6 5.37 5.45 5.55 5.65 5.80 5.92
7 5.72 5.81 5.91 6.03 6.19 6.33
8 5.43 5.50 5.57 5.66 5.78 5.87
9 4.65 4.74 4.85 4.97 5.14 5.28
10 4.36 4.48 4.62 4.78 5.00 5.18
11 3.52 3.59 3.67 3,75 3.87 3.97
12 3.07 3.14 3.21 3.30 3.42 3.52
.............................................................
SUMMER 32.9 33.2 33.5 33.8 34.3 34.8
WINTER 22.5 22.7 23.0 23.4 23.8 24.2
ANNUAL 55.3 55.8 56.3 56.9 57.7 58.3
-------------------------------------------------------------








inches at Tallahassee, Florida. These data
should be interpreted as follows: in 8
years out of every 10, the monthly ETp
would not be expected to exceed 2.10
inches. Alternatively, in only 2 years out of
10 would the monthly ETp be expected to be
greater than 2.10 inches.

In Table 2, only the extreme large
values of ETp are presented. These would
be most useful to irrigation system
designers or others concerned with the maximum
expected rates of water use by crops.

Extreme small values of ETp would be of
use to water resources planners or others
concerned with the hydrologic balance
during extreme wet as well as extreme dry
years. Extreme small values of ETp can be
calculated from the extreme large values
and the mean ETp because of the symmetry of
the normal distribution. For example, to
calculate the 5% probability (that is, the ETp
that would be expected to be exceeded in 95
years out of 100) annual ETp for Miami,
subtract the difference between the 95% ETp
value and the mean from the mean. For Miami,
the 95% ETp is 61.9 inches, the mean ETp is
54.7 inches, and the difference is 7.2
inches. The 5% ETp is then 54.7 7.2 =
47.5 inches. Likewise, the 10% ETp would be
calculated as 49.1 inches using the 90% and
the mean ETp values. This procedure could be
used for monthly or seasonal values also.

Potential ET probability data and
their geographical distributions are shown
graphically in figures 1-15. The first 12
figures show monthly values. Figures 13 and
14 show winter and summer values, respective-
ly. Figure 15 shows the distribution of total
annual ETp in Florida.









In each Figure, 3 ETp values are given
at each location. In each case the upper
number is the 50% probability (or long-term
average) value, the middle number is the 80%
probability value, and the lower number is the
95% probability value.

GEOGRAPHICAL DISTRIBUTIONS OF ETp

Figures 1-15 allow the user of these
data to observe geographical influences on
ETp. Specifically, for seasonal ETp, it can
be seen that there is little variability
(approximately 10%) in the summer data
throughout the state. However, considerable
variability (approximately 30%) occurs during
the winter months when ETp values remain much
larger in south Florida than in north
Florida. The user of these data can estimate
ETp values for other locations in Florida
by interpolating between the locations
given in figures 1-15.

USE OF POTENTIAL ET PROBABILITY DATA

Historically, many systems which are
influenced by climatic parameters have been
designed for long-term average or typical
climatic conditions at a location. Among
these are agricultural irrigation systems,
drainage systems, and water allocations by
water management districts. In many cases
these systems are influenced more by
certain extreme or critical conditions than
long-term averages. For example, one could
conclude that irrigation systems are not
required in Florida by observing only long-
term average rainfall. Actually, short-term
droughts are critical to the economic
production of many crops grown in the state.








In this case and in others where climatic
influences are important, the probabilities
of occurrence of extreme events is a more
meaningful consideration for system design.

In this work the probabilities of
occurrence of extremes in ETp rates have been
calculated. It is suggested that these
values will be useful in design and manage-
ment of irrigation systems. In addition,
water management districts may find this
information useful for the process of
allocation of water to agricultural users.
As a specific example, irrigation systems
can be designed for a specific greater
probability of success and failure if they are
sized to supply water for extreme use
conditions rather than only average condi-
tions.

ACTUAL EVAPOTRANSPIRATION

In this work, only potential evapotrans-
piration rates have been calculated. Actual
evapotranspiration for a given crop may be
greater or less than ETp depending upon the
nature of the crop and its stage of
growth. To determine the actual evapotrans-
piration for a specific crop, ETp must be
mutliplied by a crop coefficient which depends
upon the crop species and stage of growth.
Crop coefficients for many crops were
presented by Doorenbos and Pruitt (1977).

Crop coefficients express the relation-
ship between the actual ET and ETp for a
specific crop. The plant surface referenced
by ETp depends upon how the ETp equa-
tion was originally derived and calibrated.
For the Penman equation the plant surface
referenced is the previously-discussed short
grass cover. Thus, for the Penman equa-








tion, crop coefficients express the ratio of
the relative water use of a specific crop
and conditions to that of a well-watered short
green grass reference crop.

Although actual ET rates for specific
crops are not presented in this publication,
potential evapotranspiration rates as
calculated by the Penman technique are useful
because they provide a reference for the
comparison of climatic conditions from one
location to another. For example, if the
actual ET during April for a crop grown near
Orlando was 4.0 inches, the actual ET can
be calculated for the same crop near
Tallahassee using the ETp data in this
publication. First calculate the ratio of
ETp at the new location (Tallahassee, in this
example) to the ETp at the location where
actual crop ET is known (Orlando). In this
example, the April ETp for Tallahasee is 4.75
inches (Table 1), and the April ETp for
Orlando is 5.62 inches. The ratio is then
4.75/5.62 = 0.845. To calculate the estimated
actual ET at Tallahassee, multiply the actual
ET known for Orlando by the previously-
calculated ratio. In this example, (4.0
inches) (0.845) = 3.38 inches would be the
estimated actual ET for April at Tallahassee.

As another example, if the actual ET
during April for the previously-discussed
crop near Orlando was 4.0 inches, then the
actual ET near Tampa wil l also be
approximately 4.0 inches. This can be deter-
mined by observing that the April ETp at
Tampa (5.56 inches) is approximately that at
Orlando (5.62 inches).

The procedure used in the previous
examples can be used to estimate actual crop
ET rates at locations throughout Florida








where those crops are grown. Also, because
the peak rate of water use by many crops is
near ETp, and this is often the critical
period of water use in terms of economic
yield, ETp rates are good indicators of the
rates of crop water use during those
critical periods.
SUMMARY

The Penman equation was used to calcu-
late monthly, seasonal, and average daily
potential evapotranspiration (ETp) rates for
nine locations in and near Florida. Twenty-
five years of weather records were analyzed.
Probabilities of occurrence of ETp ranging
from the 50% level to the 95% level were
tabulated, and a procedure was presented for
calculating other probabilities. These ETp
values can be used by irrigation managers,
irrigation system designers, and others
interested in climatic effects on crop water
use. They allow crop water requirements to
be estimated at locations throughout Florida.
These probabilities also allow irrigation
systems to be designed for a known probability
of success or failure at locations throughout
Florida.



REFERENCES

1. Clark, G.A., and A.G. Smajstrla. 1982.
Methods and Equations Used for Predicting
Potential Evapotranspiration. Agricul-
tural Engineering Dept. Research Report.

2. Doorenbos, J. and W.O. Pruitt. 1977.
Guidelines for Predicting Crop Water
Requirements. Food and Agriculture Organi-








zation of the United Nations. Rome. 144 p.

3. Penman, H.L. 1948. Natural Evaporation
from Open Water, Bare Soil and Grass.
Proceedings of the Royal Society, Series
A. 193: 120-145.

4. Jones, J.W., L.H. Al len, L.C. Hammond, J.D.
Martsolf, J.S. Rogers, S.F. Shih, and A.G.
Smajstrla. 1984. Estimated and Measured
Evapotranspiration for Florida Conditions
and Crops. IFAS Technical Bulletin. In
Press.








PaTjr T


This public document was promulgated at a cost of $1053.48, or 35.1
Scents per copy, to provide information about potential evapotranspira-
tion probabilities and distributions in Florida. 06-3M-84



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DA, INSTITUTE OF FOOD AND AGRICULTURAL SCIENCES, K. R.
Tefertlller, director, In cooperation with the United States Department F
of Agriculture, publishes this Information to further the purpose of the
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