275 Vol. 102, No. 5; May 2013 NATIONALPARK-FORSCHUNG IN DER SCHWEIZ (Switzerland Research Park Journal)

Evaluation of HYDRUS-1D and SWAP Models in Simulating Water and Minerals Movement in Soil Ismail MoghabeliDamaneh1, FarzadHasanpour2, Mehdi Behrouj3 1Teacher, Irrigation and Drainage, Agriculture Training Center of Southern Kerman, Kerman, Iran (Corresponding author). (2 Irrigation Ph.D., Faculty Member, Irrigation Engineering Group, University of Zabol, Zabol, Iran 3 Teacher, Agronomy and Plant Breeding, Agriculture Training Center of Southern Kerman, Kerman, Iran Abstract:Itisexpensiveandtime-consumingtoconductfieldstudiesfordetermining optimumwateruselevelforrecoveringsoilsalinity.Therefore,theuseofcomputerized modelsofleachingiscommon.But,theresultsofthesemodelsmustbecomparedwith thoseoffieldstudiesbeforetheirapplication.HYDRUS-1DandSWAPmathematicalmodels areamongthemostlyutilizedcapablemodelsintheworld.Thepresentstudyevaluates these two computerized models in simulating soil moisture and salinity profile. The leaching testswereconductedinfourtreatmentsonthebasisofaRandomizedCompleteBlock Design.Then,moistureandsalinitylevelsatdifferentdepthsandtimeswerepredictedby bothHYDRUS-1DandSWAPmodels,too.Theresultsofthesemodelswerecomparedwith thoseoffieldstudiesandfinally,themodelswereevaluatedbydataanalysis.Statistical indicesshowedthatbothmodelsgaveanacceptableestimationofmoisturecontentand hadcoefficientsofcorrelationof>83%underalltreatments.Bothmodelspredicted moisturecontenthigherthanthemeasuredcontent,butHYDRUS-1Dresultedinbetter estimationofmoisturecontentthanSWAP.ThecoefficientofcorrelationofSWAPforthe estimationofsalinitywas>83%underalltreatments.Thismodelwasmuchbetterthan HYDRUS-1Dinthissense.ThemajorshortcomingofHYDRUS-1Dwasitsincapabilityin estimatingsalinityatthedepthsof>40cmduringthefirstdaysofthetests.Butoverthe time,itgaveacceptableestimations.HYDRUS-1Dpredictedtheconcentrationofminerals lower and SWAP predicted it higher than the measured one. Keywords: Simulation, moisture, salinity, model, HYDRUS-1D, SWAP 276 1.IntroductionLeachingwithwaterforrecoveringlandsneedsagreatdealofwater(typicallyabout1-m-deepwater)andthisgreatdealofwatershouldbeappliedinarelativelyshorttimetobe able torecover the soil. Prior knowledge oflocalsoil capabilitiesregarding thereductionof salinity and the effect of applied water volume unit on reducing soil salinity can be effective in planning the required water for this operation and saving water. Field studies are helpful in determining and analyzing different irrigation managements. But, theyareexpensiveandtime-consuming.Onthecontrary,simulationmodelscanbeused withmuchlowerexpenseandinshortertimeafterbeingcalibratedfordifferentirrigation options. SWAP is a one-dimensional model whose new version is user-friendly and has the capability ofsimulatingthemovementofwater,mineralsandheatandplanningtheirrigation.Itis mostlyusedinlargescales.Thegoverningequationsofthismodelaresolvedbyfinite differences method. This model does not include some important processes involved in the movement of minerals like the absorption of minerals by soil particles and the deterioration and dissolution of minerals. Kiani and Homayi (2007) evaluated SWAP model in wheat fields of Northern Gorgan, Iran for twogrowingseasons(2001-2002and2002-2003).Theirstatisticalanalysesshowedthat SWAP simulated quite well soil moisture and salinity and the relative yield of wheat despite ofthepresenceofdiversevariablesinvolvedinfieldconditions.Theyreportedthatthe coefficientofcorrelationwasover80%inallcasesandthemeansofsquaresoferrorwas lower than the standard deviation of the data. Gosevetal.(2003)estimatedtheradiation,heatandwaterexchangebetweensteppe ecosystems and atmosphere in Oklahoma and found that SWAP model simulated quite well thedynamicsofland-surfaceheatandmoistureexchangewiththeatmosphereinsteppe ecosystems(bothnaturalandagricultural)evenintheabsenceofsomeofitsparameters andso,theyconcludedthatitisfeasibletouseitinhydrological,meteorologicaland ecological studies in similar environments. Utsetetal.(2004)comparedtheevapotranspirationcalculatedbyPenman-Monteithand Priestley-TaylorapproacheswiththatsimulatedbySWAPmodelinZaragoza,Spainand foundthatthemodelwasquitewellinpredictingevapotranspirationprovidedthatsound input data were supplied. EvaluatingtheestimationofsoilmoisturebySWATREmodel,Fechteretal.(1991)reported thattheabsoluteerrorbetweenthemeasuredandsimulatedvalueswas3.5%.Fariaetal. (1992)reportedabsoluteerrorbetweenthevalueofsoilmoisturesimulatedbySWACROP modelandthemeasuredvalueastobe2.8-3.6%.Droogersetal.(2000)evaluatedSWAP modelfortheestimationofsoilmoistureunderthecultivationofcottonandwheatand foundthecoefficients(R2)forcottonandwheatastobe67and84%,respectively.Inan evaluationofSWAPmodelinsimulatingwaterandmineralsmovementinsoilprofile,Kiani andHomayi(2007)foundR2andMAEofmoistureastobe65and1.7%v/v,respectively, and for salinity as to be 64% and 0.7 ds m-2, respectively. Persicaniet al. (1996) studied the movement of Atrazine into series of alluvial soils. Atrazine at the rate of 1.4 kg ha-1 was applied to soil and then, the soil was leached by rainfall. After one month, samples were taken from different depths and their Atrazine concentration was measured.Then,AtrazinemovementrateinsoilprofilewasestimatedbyHYDRUSmodel 277 andtheresultwascomparedwiththemeasuredrates.Thebestestimationwaswhen Freundlichmodel defined Atrazine absorption and coefficients of degradation were the same forsolidandliquidphasesofthesoil.Sensibilitytestundertheconsiderationofcoefficient of absorption, coefficient of degradation and hydrological properties of soil revealed that the model was the most sensible to coefficient of absorption out of all parameters. CaseyandSimunk(2001)studiedthemovementoftrichloroethyleneinsoilcolumnand inversely estimated the parameters of CDE model by HYDRUS software. The compound was reducedinsoilandunderwentasequentialtransformationintoothercompoundsandthe finaltraceablecompoundwasethylene.Theyfoundthatincludingnonequilibriumsorption gavebetterestimationofcoefficientofdegradationandreactionratethanincluding equilibrium sorption. Ying Ma et al. (2011) applied SWAP model for simulating water cycle in water deficit lands of China and concluded that the model was a strong tool for simulating field water cycle. Verdinejadetal.(2009)appliedSWAPmodelfordeterminingtheoptimumdepthof irrigationofcropsundersalinityconditions.Dehghanetal.(2010)appliedSWAPmodelfor predictingsoilprofilemoistureinthreewheatfieldsandfoundabsoluteerrormagnitude and the roots of means of squares of errors to be 1.5-2.9% and 1.9-4, respectively. 2.Materials and Methods ThepresentstudywascarriedoutinAgriculturalResearchStation(Long.5725E.,Lat. 2730 N., Alt. 627 m, mean precipitation of 140 mm) of Jiroft, Iran. The procedure of the study was as follows: firstly, a profile was dug in experimental field and the samples were taken from different depths to determine hydrological factors and physical properties of soil profile. Then, the soil was studied and the number of soil layers per depth was determined. The different depths included 0-20, 20-40, 40-60, 60-80 and 80-100 cm and theywerestudiedintermsofthenumberofdifferentlayers,texture,apparentspecific weight, EC, coefficient of saturated water penetration (Ks), and moisture curve. To precisely determinetheamountofwaterappliedfortheplots,theplotswereplumbedtowater pump station long which water volume contours were mounted. To study the trend of soil profile moisture variations, the following treatments were applied at five replications: T1: 10 times irrigation at the rate of 100 mm and the intervals of 3 days T2: 7 times irrigation at the rate of 150 mm and the intervals of 4 days T3: 5 times irrigation at the rate of 200 mm and the intervals of 6 days T4: 4 times irrigation at the rate of 250 mm and the intervals of 7 days Theplotswere22m2spaced1mapartandtheblockswerespaced2mapart.Theinitial samplingwasconductedbeforetheonsetofleaching.Thesamplesweretransferredto laboratorytodeterminetheinitialmoistureatfivestudieddepths.Thesoilwassampled from the depths of 0-20, 20-40, 40-60, 60-80 and 80-100 cm at the onset of the study and at different stages in order to read the moisture. The soil was sampled in 10-day intervals. They were taken from five depths in each plot and from two spots in each depth. Afterenteringtheinputsofthemodel,theywererunandtheoutputsweretakensothat thevaluesofmoistureandsalinityat10th,20thand30thdayaftertheonsetofexperiment were simulated in relation to depth in one single graph. 278 Outofthefourexperimentaltreatments,treatmentT1wasusedforthecalibrationofthe model. This treatment was irrigated at the rate of 100 mm 10 times once three days. During the calibration of the models, some coefficients were so much changed that the best fitting was finally obtained between the measured values of moisture with the values simulated by themodel.Then,theywereevaluatedbyplottingmethodandstatisticalindices.The calculated indices included: 1.Fitting appropriateness coefficient (R2) =ninini iP OP OR121221 2) ((1) 2.Coefficient of determination (CD) ( ) ( ) =niniO P O O CD1212/ (2) 3.Magnitude of absolute error (MAE) n P O MAEni i/1|.|

\| = (3) 4.Root of mean squares of error (RMSE) ( )2 112/|.|

\| = ni in O P RMSE (4) 5.Standard deviation (Sd) ( )2 / 112/|.|

\| = n O O Sni d(5) 6.Coefficient of remaining mass (CRM) ||.|

\| =nini inO P O CRM1 1 1/ (6) 7.Efficiency of modeling (EF) ( ) ( ) ( ) |.|

\| |.|

\| = nin ni i iO O O P O O EF121 122/ (7) where,Pidenotesthemoistureorsalinitysimulatedbymodels,Oidenotesthemeasured moistureorsalinity,O denotesmeanmeasuredmoistureorsalinity,andndenotesthe number of spots. 3.Results and Discussion SensitivityanalysisofHYDRUS-1Dmodelshowedthatthemodelwasverysensitiveto saturation moisture, diffusion coefficient and coefficient and was sensitive to coefficient n. Also,SWAPmodelwasfoundtobeverysensitivetoirrigationwaterconcentrationand pondingcoefficientandrelativelysensitivetodiffusionlength.Therefore,thesequantities were precisely measured again. Out of the four studied treatments, T1was used for the calibration of HYDRUS-1D and SWAP models. 279 Inthissection,themeasuredmoistureandsalinitylevelswerecomparedbythemoisture andsalinitylevelspredictedbythesemodelsusingbothplottingmethodandstatistical indices. 4.Plotting comparison Measured vs. models-predicted moisture Thefollowinggraphspresentthemeasuredmoisturelevel,themoisturelevelpredictedby HYDRUS-1DmodelandthemoisturelevelpredictedbySWAPmodelfordifferentdepths. The symbols O, H and S denote the measured moisture, the moisture predicted by HYDRUS-1DmodelandthemoisturepredictedbySWAPmodel,respectively.Inaddition,theletters a,bandcshowthe10th,20thand30thdayoftheexperiment,respectively.Sincethefirst treatment was used for calibrating the models, its results are not presented. Measured and models-simulated moisture level relative to depth under the treatment of T2 Measured and models-simulated moisture level relative to depth under the treatment of T3 Measured and models-simulated moisture level relative to depth under the treatment of T4 Drawn by MS-Excel software, the following graphs show the measured and simulated levels of moisture separately in which coefficients of correlation are given. 280 Measuredvs.HYDRUS-1D-simulatedmoisturebyHYDRUS-1Dmodelunderdifferent treatments Measured vs. SWAP-simulated moisture under different treatments Measured vs. models-simulated moisture under all treatments Measured vs. models-predicted salinity Measuredvs.models-simulatedsalinitylevelbymodelsrelativetodepthunderthe treatment of T2 281 Measured vs. models-simulated salinity level relative to depth under the treatment of T3 Measured vs. models-simulated salinity level relative to depth under the treatment of T4 The following graphs drawn by MS-Excel show the measured and simulated levels of salinity in one single graph and their coefficients of correlation. Measured vs. HYDRUS-1D-simulated salinity under different treatments Measured vs. SWAP-simulated salinity under different treatments 282 Measured vs. models-simulated salinity under all treatments 5.Statistical indices ThestatisticalindicesforthestudyandcomparisonoftheperformanceofHYDRUS-1Dand SWAP models in predicting moisture and salinity are presented in the following table. Statistical indices HYDRUS-1D model SWAP model R2 0.70610.4826 CD0.28900.2237 MAE0.02800.0664 CRM-0.0184-0.2457 RMSE0.03900.0707 Sd 0.03370.0337 EF-0.3431-3.4098 Statistical evaluation of HYDRUS-1D and SWAP models in simulating the movement of minerals Statistical indices HYDRUS-1D model SWAP model R2 0.05360.6334 CD0.06231.1663 MAE0.08980.0127 CRM0.1318-0.0095 RMSE0.15760.0201 Sd 0.03660.0366 EF-17.53570.6982 R2showsthefitting,CDwhichisdifferentfromR2innatureshowstheratioofdistribution betweenthesimulatedvaluestothedistributionbetweenthemeasuredvalues,andMAE showstheprecisionofthemodel.RMSEshowshowmuchthesimulationsarehigheror lowerthanthemeasurements.CRMdenotesthetendencyofthemodelinover-or underestimation of the measured values. Finally, EF compares the simulated values with the measuredones.WhenEFisnegative,itimpliesthatmeanmeasuredvaluegivesbetter 283 predictionthanthesimulatedone.WhenRMSE