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20120140502010
20120140502010
20120140502010
20120140502010
20120140502010
20120140502010
20120140502010
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20120140502010

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  • 1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 87-94, © IAEME 87 EVALUATION AND CALIBRATION OF TEMPERATURE BASED METHODS FOR REFERENCE EVAPOTRANSPIRATION ESTIMATION IN TIRUPATI REGION K. Chandrasekhar Reddy Professor and Principal, Department of Civil Engineering, Siddharth Institute of Engineering & Technology, Puttur, Andhra Pradesh, India. ABSTRACT The study aims to recalibrate various temperature based reference evapotranspiration (ET0) estimation methods in Tirupati region of Andhra Pradesh, India. Meteorological data observed at the Tirupati Agricultural Research Station were collected from the India Meteorological Department (IMD), Pune. The temperature based methods of Blaney- Criddle, Hargreaves and Jensen-Haise, selected for the present study, were evaluated with reference to standard FAO-56 Penman- Monteith(PM) method. The relationships between PM method and other methods were developed to obtain monthly ET0 estimates comparable with PM method. The ET0 equations were recalibrated with respect to PM method for improving their monthly ET0 estimation capability in the region selected for the present study. The recalibrated Blaney-Criddle method showed satisfactory performance in the monthly ET0 estimation. So, it may be adopted for the study area because of its simpler data requirements with reasonable degree of accuracy. Keywords: Temperature Based Methods, Penman-Monteith, Recalibration, Reference Evapotranspiration. 1. INTRODUCTION Accurate estimation of Evapotranspiration is of paramount importance in water supply requirements of proposed irrigation projects for water balance studies, irrigation system design, crop yield simulation and water resources planning and management. It is desirable to have a method that estimates reasonably the reference Evapotranspiration (ET0). Most of the studies have shown that the FAO-56 Penman-Monteith (PM) method gives very accurate ET0 estimates in different environments. However, under limited climatic data availability conditions, the simple empirical methods yielding results comparable with PM ET0 may be selected at regional level for reasonable estimation of ET0. This study deals with the evaluation of ET0 estimation methods by comparing INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 5, Issue 2, February (2014), pp. 87-94 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2014): 4.1710 (Calculated by GISI) www.jifactor.com IJARET © I A E M E
  • 2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 87-94, © IAEME 88 their performance with that of PM method, developing relationships between PM and other methods and recalibrating the methods with respect to PM method. Mallikarjuna and ArunaJyothy (2008)[7] evolved the performance of various empirical methods for estimating ETc for different crops for Tirupati and Nellore regions of Andhra Pradesh. Suleiman and Hoogenboom (2007)[10] made a study to assess the potential improvement that can be achieved by replacing Priestley – Taylor with FAO – 56 Penman-Monteith in Georgia and southern states in a humid climate of mountainous and coastal areas. Demirtas et al. (2007)[2] developed regional relationships between ET and that estimated by various climatological methods and concluded that Penman-Monteith method gives the best results followed by Penman, Radiation and Blaney-Criddle methods. Nandagiri and Kovoor (2006)[8] evaluated the performance of several ET0 methods in the major climatic regions of India and identified that the FAO – 56 Hargreaves (temperature based) method yielded ET0 estimates closest to the FAO – 56 Penman-Monteith method in all the climates except the humid one where the Turc (radiation based) method was the best. Temesgen et al. (2005)[11] compared ET0 equations and indicated that ET0 estimated by California Irrigation Management Information System (CIMFIS) Penman equation correlated well with those estimated by standardized Penman – Monteith equation. Irmak et al. (2003)[4] recommended solar radiation and net radiation based ET0 equations over the other commonly used temperature and radiation based methods by comparing their performance with Penman-Monteith method. The present study reports the performance evaluation of commonly used temperature based ET0 estimation methods based on their accuracy of estimation and development of inter-relationships between the Penman-Monteith and the other climatological variables. And also, these methods are recalibrated with Penman-Monteith method for the Tirupati region of Andhra Pradesh. 2. MATERIALS AND METHODS Tirupati region, located in Chittoor district of Andhra Pradesh, India, with global coordinates of 130 05’N latitude and 790 05’ E longitudes, has been chosen as the study area. The meteorological data at the region for the period 1992-2001 were collected from IMD, Pune. Data from 1992 to 1998 is used for the purpose of training the model and that of 1999 to 2001 for testing the model. The details of the methods selected for the present study are presented in Table 1. Table1: Details of reference evapotranspiration estimation methods Method Basic reference Equation Input data Primary Secondary FAO 56 Penman- Monteith (PM) Allen et al., (1998)[1] ET0 = )34.01( )( 273 900 )(408.0 2 '' '' 2 '''' u eeu T GR as mean n ++∆ − + +−∆ γ γ Tmax, Tmin, RHmax, RHmin, u2, n --- Temperature based methods 1.FAO24 Blaney- Criddle(BC) Doorenbos and Pruitt (1977)[3] ET0 = a +b [p (0.46T + 8.13)] Where a = 0.0043 (RHmin) – n/N – 1.41 b = 0.82 – 0.0041 (RHmin) + 1.07 (n/N) + 0.066(ud) – 0.006 (RHmin) (n/N) – 0.0006 (RHmin)(ud) P, T RHmin, n, u2, ud/un 2. Jensen- Haise(JH) Jensen-Haise (1963)[5] ET0 = Rs (0.025 T + 0.08) T, n --- 3.FAO-56 Hargreaves (HR) Allen et al., (1998)[1] ET0= 0.0023 Ra (T + 17.8) x (TD) 0.5 Tmax, Tmin, n ---
  • 3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 87-94, © IAEME 89 3. PERFORMANCE EVALUATION CRITERIA The performance evaluation criteria used in the present study are the coefficient of determination (R2 ), the root mean square error (RMSE), systematic RMSE, unsystematic RMSE and the efficiency coefficient (EC). 3.1 Coefficient of Determination (R2 ) It is the square of the correlation coefficient (R) and the correlation coefficient is expressed as Where O and P are observed and estimated values, O and P are the means of observed and estimated values and n is the number of observations. It measures the degree of association between the observed and estimated values and indicates the relative assessment of the model performance in dimensionless measure. 3.2 Root Mean Square Error (RMSE) It yields the residual error in terms of the mean square error and is expressed as (Yu et al., 1994)[12] n op RMSE ii n i 2 1 )( − = ∑= 3.3 Systematic RMSE (RMSEs) It measures the room available for local adjustment. It is expressed as n op RMSE ii n i s 2 1 )ˆ( − = ∑= Where ii boap +=ˆ , a and b are the liner regression coefficients 3.4 Unsystematic RMSE (RMSEu) It shows the noise level in the model and is a measure of scatter about the regression line and potential accuracy. It is expressed as n pp RMSE ii n i u 2 1 )ˆ( − = ∑= 2/1 1 2 1 2 1 )()( ))((     ∑ −∑ − −−∑ = == = n i i n i i ii n i ppoo ppoo R
  • 4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 87-94, © IAEME 90 3.5 Efficiency Coefficient (EC) It is used to assess the performance of different models (Nash and Sutcliffe, 1970)[9] . It is a better choice than RMSE statistic when the calibration and verification periods have different lengths (Liang et al., 1994)[6] . It measures directly the ability of the model to reproduce the observed values and is expressed as ( ) ( )∑ ∑ = = − − −= n i i n i ii oo po EC 1 2 1 2 1 A value of EC of 90% generally indicates a very satisfactory model performance while a value in the range 80-90%, a fairly good model. Values of EC in the range 60-80% would indicate an unsatisfactory model fit. 4. RESULTS AND DISCUSSION The percentage deviations of ET0 values estimated by BC, JH and HR methods with reference to PM method are presented in Table 2. It may be observed that the deviations are significant for all the methods. Among the methods, the performance of BC is relatively better than JH and HR methods in the study area. This may be due to the inability of JH and HR methods to account for the effects of wind speed and vapour pressure deficit under advective conditions in semi- arid environment. Fig.1 showing the comparison of ET0 estimates with those of PM ET0 also exhibit similar observations. Table 2 Percentage deviations in the monthly average ET0 values estimated by temperature based methods with PM method Percentage deviation BC JH HR -18.3 to 17.8 -23.9 to 42.6 -28.0 to 25.9 Fig.1 Comparison of monthly average ET0 values estimated by temperature based methods with PM method 4.1 Development of inter-relationships between PM method and temperature based methods The ET0 values estimated using BC, JH and HR methods plotted against PM ET0 are shown in Fig.2 for study area. The performance indicators of the relationships developed between PM method and these methods are presented in Table 3. It may be observed that the BC ET0 values converted into equivalent PM ET0 values using the relationship between PM and BC methods are Tirupati 0 1 2 3 4 5 6 7 8 9 10 11 1 11 21 31 41 51 61 71 81 91 101 Months ETo(mm/day) PM BC JH HR
  • 5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 87-94, © IAEME 91 mostly comparable with those computed using PM equation. This may be due to the fact that wind velocity and relative humidity are also used in BC method as secondary input. However, these methods showed a large bias in the slope and intercept of scatter plots. Also, the systematic RMSE and unsystematic RMSE which respectively represent the scope for recalibration and the amount of scatter about the regression line are relatively more. It may therefore be felt that the methods’ performance can further be improved by suitably recalibrating them against PM method using the observed climatic data. Fig.2 Scatter plots of monthly average ET0 values estimated by temperature based methods against PM method Table 3: Performance indicators of temperature based methods with reference to PM method Method Relationship R2 RMSE (mm) RMSES (mm) RMSEU (mm) EC (%) BC PM = 0.9126 BC +0.5275 0.8763 0.48 0.17 0.45 87.63 JH PM = 0.8123 JH + 0.3162 0.6463 0.81 0.48 0.65 64.63 HR PM = 1.2757 HR –1.1610 0.7956 0.62 0.28 0.55 79.56 4.2 Recalibration of temperature based ET0 estimation methods It has been emphasized in the above section that temperature based methods selected for the present study have not performed satisfactorily in the regional ET0 estimation. The relationships developed between PM method and these methods to estimate ET0 comparable with PM method in the regions as presented in the above section also showed unsatisfactory performances, though there was an improvement over the original methods. Therefore, before applying these methods to other regions, it is necessary to recalibrate them based on the locally collected lysimeter measured ET0 data accompanied by meteorological data such that they can be used in the regions of the study area for reliable ET0 estimation. However, in the absence of lysimeter data, the competent PM method is usually adopted as the standard method of comparison for recalibration of the other methods. Since lysimeter measured ET0 data is not available in most of the regions, the methods were recalibrated with respect to PM method. The BC, JH and HR methods selected for the present study were recalibrated with reference to PM method. The recalibrated equations derived for region of the study area are presented along with original equation in Table 4. From this table it may be observed that there is a significant variation on recalibration of coefficients compared to the original values. The performance indicators of recalibrated equations in the estimation of ET0 for both training and testing periods are given in Table 5. The scatter and comparison plots of ET0 values estimated by these methods with those of PM ET0 during the testing period are shown in Fig.3 and Fig.4 respectively. It may be observed from Table 5 that the BC method yielded the least RMSE and resulted in ET0 comparable with PM method. The BC method outperformed these methods in terms of Tirupati 0 1 2 3 4 5 6 7 8 9 10 11 0 1 2 3 4 5 6 7 8 9 10 11 ETo by HR, mm/day ETobyPM,mm/day Tirupati 0 1 2 3 4 5 6 7 8 9 10 11 0 1 2 3 4 5 6 7 8 9 10 11 ETo by BC, mm/day ETobyPM,mm/day Best fit line Ideal line Tirupati 0 1 2 3 4 5 6 7 8 9 10 11 0 1 2 3 4 5 6 7 8 9 10 11 ETo by JH, mm/day ETobyPM,mm/day
  • 6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 87-94, © IAEME 92 performance evaluation criteria. The slope and intercept respectively close to one and zero also indicate an improved performance of the method with recalibrated coefficients. From the above discussion, it may be concluded that the BC method with recalibrated coefficients may be used for ET0 estimation. However, JH and HR methods may also be adopted with recalibrated coefficients for reasonable ET0 estimation depending upon the data availability. Table 4 Recalibrated temperature based ET0 equations Method Original Equation Recalibrated Equation 1.FAO24 Blaney- riddle(BC) Method ET0 = a +b [p (0.46T + 8.13)] Where a = 0.0043 (RHmin) – n/N – 1.41 b = 0.82 – 0.0041 (RHmin) + 1.07 (n/N) + 0.066(ud) – 0.006 (RHmin) (n/N) – 0.0006 (RHmin)(ud) ET0 = a +b [p (0.46T + 8.13)] where a = – 0.0530 (RHmin) – 4.18 (n/N) + 3.1 b = – 0.08 + 0.0087 (RHmin) + 1.26 (n/N) + 0.156 (ud) – 0.003 (RHmin) (n/N) – 0.0015 (RHmin) (ud) 2. Jensen- Haise(JH) Method ET0 = Rs (0.025 T + 0.08) ET0 = Rs (0.035 T – 0.31) 3.FAO-56 Hargreaves (HR)Method ET0 = 0.0023 Ra (T + 17.8) x (TD) 0.5 ET0 = 0.0048 Ra (T– 4.8) x (TD)0.5 Table 5 Performance indices of recalibrated temperature based ET0 methods Method Slope of the scatter plots Intercept of the scatter plots R2 RMSE (mm) EC (%) Training Period Testing period Training period Testing period Training period Testing period Training Period Testing period Training period Testing period BC 1.0000 1.0253 0.0007 -0.1490 0.9947 0.9961 0.10 0.08 99.47 99.61 JH 0.8287 0.8045 0.3663 0.8936 0.7399 0.7256 0.69 0.70 73.99 72.56 HR 0.9517 0.9494 0.2741 -0.0060 0.8432 0.8507 0.54 0.52 84.32 85.07 Fig.3 Scatter plots of monthly average ET0 values estimated by recalibrated temperature based methods against PM ET0 values during testing period Tirupati 0 1 2 3 4 5 6 7 8 9 10 11 0 1 2 3 4 5 6 7 8 9 10 11 ETo by BC, mm/day ETobyPM,mm/day Ideal line Best fit line Tirupati 0 1 2 3 4 5 6 7 8 9 10 11 0 1 2 3 4 5 6 7 8 9 10 11 ETo by JH, mm/day ETobyPM,mm/day Tirupati 0 1 2 3 4 5 6 7 8 9 10 11 0 1 2 3 4 5 6 7 8 9 10 11 ETo by HR, mm/day ETobyPM,mm/day
  • 7. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 87-94, © IAEME 93 Fig.4 Comparison of monthly average ET0 values estimated by recalibrated temperature based methods with those estimated by PM method during testing period 5. CONCLUSION The percentage deviations of ET0 values estimated by BC, JH and HR methods with reference to PM method are significant. The BC method slightly improved its performance over interrelationships in terms of evaluation criteria in the region. The recalibrated BC method performed well and it may be used for ET0 estimation in similar climatic regions. However, recalibrated JH and HR methods may also be adopted for reasonable ET0 estimation depending upon the data availability. REFERENCES [1] Allen, R. G., Pereira, L. S., Raes, D. and Smith, M. (1998), Crop evapotranspiration - Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, FAO, Rome. pp.326. [2] Denmirtas, C., Buyukcangaz, H., Yazgan, S., Candogan.B.N. (2007), Evaluation of Evapotranspiration Estimation Methods for Sweet Cherry Trees (Prunus avium) in Sub-humid Climate. Pakistan Journal of Biological Sciences, Vol.10 (3), pp. 462-469. [3] Doorenbos, J. and Pruitt, W.O. (1977), Guidelines for predicting crop water requirements. FAO Irrigation and Drainage Paper No.24, FAO, Rome, Italy. [4] Irmark, S., Irmak, A., Allen, R. G., and Jones, J. W., (2003), Solar and Net Radiation - Based Equations to estimate Reference Evapotranspiration in Humid Climates. Journal of Irrigation and Drainage Engineering, ASCE, Vol.129, No.5. pp. 336-347. [5] Jensen, M. E. and Haise, H. R. (1963), Estimating evapotranspiration from solar radiation. Journal of Irrigation and Drainage Engineering, ASCE, Vol.89, No.4, pp.15-41. [6] Liang, G. C., O-Connor, K. M. and Kachroo, R. K. (1994), a multiple-input single-output variable gain factor model. Journal of Hydrology, Vol.155, No.1-2, pp.185-198. [7] Mallikarjuna, P. and Aruna Jyothy, S. (2008), Evapotranspiration Studies for irrigation Projects-A case Study. Journal of the Institution of Engineers (India), Agriculture Engineering Division, vol. 89, pp.5-13. [8] Nandagiri, L. and Kovoor, M, G. (2006), Performance Evaluation of Reference Evapotranspiration Equations across a Range of Indian Climates. Journal of Irrigation and Drainage Engineering, ASCE, Vol.132, No.3. pp. 238-249. [9] Nash, J. E. and Sutcliffe, J. V. (1970), River flow forecasting through conceptual models part I: A discussion of principles. Journal of Hydrology, Vol.10, No.3, pp.282-290. Tirupati 0 1 2 3 4 5 6 7 8 9 10 11 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Months ETo(mm/day) PM BC JH HR
  • 8. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 2, February (2014), pp. 87-94, © IAEME 94 [10] Suleiman, A. and Hoogenboom, G.(2007), Comparison of Priestley-Taylor and FAO-56 Penman-Monteith for Daily Reference Evapotranspiration Estimation in Georgia. Journal of Irrigation and Drainage Engineering, ASCE, Vol.133, No.2. pp. 175-182. [11] Temesgen, B., Eching, S., Davidoff, B., and Frame, K.,4 et al. (2005). Comparison of Some Reference Evapotranspiration Equations for California. Journal of Irrigation and Drainage Engineering, ASCE, Vol.131, No.1. pp. 73-84. [12] Yu, P. S., Liu, C. L. and Lee, T. Y. (1994), Application of transfer function model to a storage runoff process. In Hipel K. W., McLeod A.I. and Panu U.S. (Ed.), Stochastic and Statistical Methods in Hydrology and Environmental Engineering, Vol.3, pp.87-97. [13] Sameer Ul Bashir, Younis Majid and Ubair Muzzaffer Rather, “Effect of Rapidite on Strength of Concrete in Warm Climates”, International Journal of Civil Engineering & Technology (IJCIET), Volume 4, Issue 6, 2013, pp. 126 - 133, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316. [14] P.C.Madhuraj and J.Sudhakumar, “Assessment of Transient Hygroscopic Behaviour for Design of Passive Solar Building Envelope for Hot-Humid Regions”, International Journal of Civil Engineering & Technology (IJCIET), Volume 1, Issue 1, 2010, pp. 46 - 54, ISSN Print: 0976 – 6308, ISSN Online: 0976 – 6316.

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