Estimation of Reference Evapotranspiration using Regionally Calibrated Hargreaves-Samani Equation

Mamoon ur Rasheed

Authors

Mamoon ur Rasheed Department of Space Science, University of the Punjab, Lahore, Pakistan

Keywords:

Reference Evapotranspiration (ETO), Hargreaves-Samani, Shuttle Radar Topography Mission (SRTM) and Landsat 8

Abstract

Evapotranspiration (ETO) is a significant module in water-balance, irrigation scheduling and estimation of crop water requirement models. ETO can be adequately assessed when meteorological data are accessible to implement robust and strong models such as FAO-56 Penman-Monteith (PM). However, due to data insufficiency, substitute methodologies are essential. In this context, this study aims to calculate ETO from regionally calibrated Hargreaves-Samani (HSCAL), Hargreaves-Samani (HS) and Hargreaves methods which base on Land Surface Temperature (LST) and Solar Radiation (SR). SR was calculated from empirical formulas and Shuttle Radar Topography Mission (SRTM) 30m Digital Elevation Model (DEM). HSCAL uses SR which calculated from empirical formulas as an input, whereas HS and Hargreaves uses SR which calculated from the SRTM 30m DEM. LST was calculated from Landsat8 (LS8) thermal band for all three methods. Furthermore, ETO obtained from the HSCAL (ETO,HSCAL) was compared with standard FAO-ETO values and after verification HSCAL treated as standard for the verification of the remaining two methods on various Land Use Land Cover (LULC) types. Results of comparison between ETO,HSCAL and standard FAO-ETO shows that mostly values are within the range but lower side. Comparison also disclose that vegetation and built-up LULC are the best and worst case respectively. Further, ETO,HSCAL values are mostly fall within lower class of the ranges during the monsoon season (August-September). Further, the performance of the HS and Hargreaves are evaluated based on statistical indicators; Root Mean Square Error (RMSE), Mean Bias Error (MBE), Mean Absolute Error (MAE) and Correlation Coefficient (R2). ETO values of HS (ETO,HS) and Hargreaves (ETO,H) are underestimated in the sami-arid climate zone. The mean values of all statistical indicators are lower for ETO,HS in comparison to ETO,H when ETO,HSCAL is used to compare ETO,H with ETO,HS. It indicates that, in comparison to ETO,H, ETO,HS is close to ETO,HSCAL.

References

M. H. Abd El-Wahed and T. Ali, “Estimating reference evapotranspiration using modified Blaney-Criddle equation in arid region Response of antioxidative defense system in plants grown under some environmental stresses to some antioxidants application View project salinity stress View project”, Accessed: Jul. 13, 2022. [Online]. Available: https://www.researchgate.net/publication/281281160

M. A. El-Shirbeny, A.-E. M. Ali, and N. H. Saleh, “Crop Water Requirements in Egypt Using Remote Sensing Techniques,” J. Agric. Chem. Environ., vol. 03, no. 02, pp. 57–65, 2014, doi: 10.4236/jacen.2014.32b010.

X. Song, J. Zhang, C. Zhang, and X. Zou, “A Comprehensive Analysis of the Changes in Precipitation Patterns over Beijing during 1960-2012,” Adv. Meteorol., vol. 2019, 2019, doi: 10.1155/2019/6364040.

S. A. Bhat, B. Pandit, M. U. Din Dar, R. Jan, S. Khan, and K. Mehraj, “Statistical Comparison of Reference Evapotranspiration Methods: A Case Study from Srinagar in J&K, India,” Int. J. Curr. Microbiol. Appl. Sci., vol. 6, no. 9, pp. 3731–3737, Sep. 2017, doi: 10.20546/IJCMAS.2017.609.460.

J. Patel, H. Patel, and C. Bhatt, “Modified Hargreaves Equation for Accurate Estimation of Evapotranspiration of Diverse Climate Locations in India,” Proc. Natl. Acad. Sci. India Sect. B - Biol. Sci., vol. 85, no. 1, pp. 161–166, Mar. 2015, doi: 10.1007/S40011-014-0314-Y.

R. Habeeb et al., “Statistical analysis of modified Hargreaves equation for precise estimation of reference evapotranspiration,” New pub Stock. uni Press, vol. 73, no. 1, pp. 1–12, 2021, doi: 10.1080/16000870.2021.1966869.

C. S. Rim, “The effects of urbanization, geographical and topographical conditions on reference evapotranspiration,” Clim. Chang. 2009 973, vol. 97, no. 3, pp. 483–514, Jul. 2009, doi: 10.1007/S10584-009-9618-Y.

P. M. Ndiaye, A. Bodian, L. Diop, and K. Djaman, “Sensitivity Analysis of the Penman-Monteith Reference Evapotranspiration to Climatic Variables: Case of Burkina Faso,” J. Water Resour. Prot., vol. 09, no. 12, pp. 1364–1376, 2017, doi: 10.4236/JWARP.2017.912087.

K. Djaman, S. Irmak, I. Kabenge, and K. Futakuchi, “Evaluation of FAO-56 Penman-Monteith Model with Limited Data and the Valiantzas Models for Estimating Grass-Reference Evapotranspiration in Sahelian Conditions,” J. Irrig. Drain. Eng., vol. 142, no. 11, p. 04016044, Nov. 2016, doi: 10.1061/(ASCE)IR.1943- 4774.0001070--.

M. J. Diamantopoulou, P. E. Georgiou, and D. M. Papamichail, “Performance evaluation of artificial neural networks in estimating reference evapotranspiration with minimal meteorological data,” Glob. Nest J., vol. 13, no. 1, pp. 18–27, 2011, doi: 10.30955/gnj.000758.

S. Celestin, F. Qi, R. Li, T. Yu, and W. Cheng, “Evaluation of 32 simple equations against the penman–monteith method to estimate the reference evapotranspiration in the hexi corridor, northwest china,” Water (Switzerland), vol. 12, no. 10, 2020, doi: 10.3390/w12102772.

S. Shamshirband et al., “Estimation of Reference Evapotranspiration Using Neural Networks and Cuckoo Search Algorithm,” J. Irrig. Drain. Eng., vol. 142, no. 2, pp. 1– 11, 2016, doi: 10.1061/(asce)ir.1943-4774.0000949.

D. K. Fisher and H. C. Pringle III, “Evaluation of alternative methods for estimating reference evapotranspiration,” Agric. Sci., vol. 04, no. 08, pp. 51–60, 2013, doi: 10.4236/as.2013.48a008.

M. Córdova, G. Carrillo-Rojas, P. Crespo, B. Wilcox, and R. Célleri, “Evaluation of the Penman-Monteith (FAO 56 PM) Method for Calculating Reference Evapotranspiration Using Limited Data,” Mt. Res. Dev., vol. 35, no. 3, pp. 230–239, Aug. 2015, doi: 10.1659/MRD-JOURNAL-D-14-0024.1.

H. Tabari, M. E. Grismer, and S. Trajkovic, “Comparative analysis of 31 reference evapotranspiration methods under humid conditions,” Irrig. Sci. 2011 312, vol. 31, no. 2, pp. 107–117, Jul. 2011, doi: 10.1007/S00271-011-0295-Z.

G. Rana and N. Katerji, “Measurement and estimation of actual evapotranspiration in the field under Mediterranean climate: a review,” Eur. J. Agron., vol. 13, no. 2–3, pp. 125–153, Jul. 2000, doi: 10.1016/S1161-0301(00)00070-8.

M. Jabloun and A. Sahli, “Evaluation of FAO-56 methodology for estimating reference evapotranspiration using limited climatic data. Application to Tunisia,” Agric. Water Manag., vol. 95, no. 6, pp. 707–715, 2008, doi: 10.1016/j.agwat.2008.01.009.

M. Córdova, G. Carrillo-Rojas, P. Crespo, B. Wilcox, and R. Célleri, “Evaluation of the Penman-Monteith (FAO 56 PM) Method for Calculating Reference Evapotranspiration Using Limited Data,” https://doi.org/10.1659/MRD-JOURNALD-14-0024.1, vol. 35, no. 3, pp. 230–239, Aug. 2015, doi: 10.1659/MRD-JOURNALD-14-0024.1.

R. Kour, N. Patel, and A. P. Krishna, “Influence of shadow on the thermal and optical snow indices and their interrelationship,” Remote Sens. Environ., vol. 187, pp. 119–129, Dec. 2016, doi: 10.1016/j.rse.2016.10.017.

N. R. Patel, B. R. Parida, V. Venus, S. K. Saha, and V. K. Dadhwal, “Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data,” Environ. Monit. Assess., vol. 184, no. 12, pp. 7153–7163, Dec. 2012, doi: 10.1007/s10661-011-2487-7.

N. Bhatt and P. V Virparia, “Comparison of Data Mining Classification Method for Predicting Crop Water Requirement,” vol. 3, no. 5, pp. 34–40, 2018.

A. Sekertekin and S. Bonafoni, “Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: Assessment of different retrieval algorithms and emissivity models and toolbox implementation,” Remote Sens., vol. 12, no. 2, 2020, doi: 10.3390/rs12020294.

M. Valipour, M. A. Gholami Sefidkouhi, and M. Raeini-Sarjaz, “Selecting the best model to estimate potential evapotranspiration with respect to climate change and magnitudes of extreme events,” Agric. Water Manag., vol. 180, pp. 50–60, Jan. 2017, doi: 10.1016/J.AGWAT.2016.08.025.

G. Mendicino and A. Senatore, “Regionalization of the Hargreaves Coefficient for the Assessment of Distributed Reference Evapotranspiration in Southern Italy,” J. Irrig. Drain. Eng., vol. 139, no. 5, pp. 349–362, 2013, doi: 10.1061/(asce)ir.1943- 4774.0000547.

S. Trajkovic, M. Gocic, R. Pongracz, J. Bartholy, and M. Milanovic, “Assessment of Reference Evapotranspiration by Regionally Calibrated Temperature-Based Equations,” KSCE J. Civ. Eng., vol. 24, no. 3, pp. 1020–1027, 2020, doi: 10.1007/s12205-020-1698-2.

L. da C. Santos, G. H. T. Cruz, F. F. Capuchinho, J. V. José, and E. F. dos Reis, “Assessment of empirical methods for estimation of reference evapotranspiration in the Brazilian Savannah,” Aust. J. Crop Sci., vol. 13, no. 7, pp. 1094–1104, 2019, doi: 10.21475/ajcs.19.13.07.p1569.

A. A. K. Muhammad Hafeez, “Assessment of Hargreaves and Blaney-Criddle Methods to Estimate Reference Evapotranspiration Under Coastal Conditions,” http://www.sciencepublishinggroup.com, vol. 3, no. 4, p. 65, 2019, doi: 10.11648/J.AJSET.20180304.11.

D. Althoff, R. A. dos Santos, H. C. Bazame, F. F. da Cunha, and R. Filgueiras, “Improvement of Hargreaves–Samani Reference Evapotranspiration Estimates with Local Calibration,” Water 2019, Vol. 11, Page 2272, vol. 11, no. 11, p. 2272, Oct. 2019, doi: 10.3390/W11112272.

J. Romualdo De Sousa Lima et al., “Calibration of Hargreaves-Samani Equation for Estimating Reference Evapotranspiration in Sub-Humid Region of Brazil,” J. Water Resour. Prot., vol. 2013, no. 12, pp. 1–5, Dec. 2013, doi: 10.4236/JWARP.2013.512A001.

J. Almorox and J. Grieser, “Calibration of the Hargreaves–Samani method for the calculation of reference evapotranspiration in different Köppen climate classes,” Hydrol. Res., vol. 47, no. 2, pp. 521–531, Apr. 2016, doi: 10.2166/NH.2015.091.

L. Borges Ferreira, F. França Da Cunha, A. B. Duarte, G. C. Sediyama, and P. R. Cecon, “Calibration methods for the Hargreaves-Samani equation Métodos de calibração para a equação de Hargreaves-Samani,” vol. 42, no. 1, pp. 104–114, doi: 10.1590/1413-70542018421017517.

S. A. A. Bokhari, G. Rasul, A. C. Ruani, G. Hoogenboom, and A. Ahmad, “The past and future changes in climate of the rice-wheat cropping zone in Punjab, Pakistan,” Pak. J. Met., vol. 13, no. 26, pp. 9–23, 2017.

G. H. Hargreaves, F. Asce, and R. G. Allen, “History and Evaluation of Hargreaves Evapotranspiration Equation”, doi: 10.1061/ASCE0733-94372003129:153.

“Chapter 1 - Introduction to evapotranspiration.” https://www.fao.org/3/x0490e/x0490e04.htm (accessed Jul. 18, 2022).

M. Z. Al-Hamdan, D. A. Quattrochi, L. Bounoua, A. Lachir, and P. Zhang, “Using Landsat, MODIS, and a biophysical model to evaluate LST in urban centers,” Remote Sens., vol. 8, no. 11, 2016, doi: 10.3390/rs8110952.

S. Biswal, A. Ghosh, R. Sharma, and P. K. Joshi, “Satellite Data Classification Using Open Source Support,” J. Indian Soc. Remote Sens., vol. 41, no. 3, pp. 523–530, 2013, doi: 10.1007/s12524-013-0265-4.

J. C. Price, “The potential of remotely sensed thermal infrared data to infer surface soil moisture and evaporation,” Water Resour. Res., vol. 16, no. 4, pp. 787–795, Aug. 1980, doi: 10.1029/WR016I004P00787.

A. Sekertekin et al., “Comparison of different types of Hargreaves equation for estimating monthly evapotranspiration in the south of Iran,” Remote Sens., vol. 12, no. 1, pp. 1–14, Jan. 2020, doi: 10.1007/978-3-030-06118-0.

S. B. Duan et al., “Estimation of Diurnal Cycle of Land Surface Temperature at High Temporal and Spatial Resolution from Clear-Sky MODIS Data,” Remote Sens. 2014, Vol. 6, Pages 3247-3262, vol. 6, no. 4, pp. 3247–3262, Apr. 2014, doi: 10.3390/RS6043247.

S. B. Duan, Z. L. Li, B. H. Tang, H. Wu, and R. Tang, “Generation of a timeconsistent land surface temperature product from MODIS data,” Remote Sens. Environ., vol. 140, pp. 339–349, Jan. 2014, doi: 10.1016/j.rse.2013.09.003.

R. Kumar, M. K. Jat, and V. Shankar, “Methods to estimate irrigated reference crop evapotranspiration – a review,” Water Sci. Technol., vol. 66, no. 3, pp. 525–535, Aug. 2012, doi: 10.2166/WST.2012.191.

M. A. Genaidy, “Estimating of Evapotranspiration Using Artificial Neural Network,” Misr J. Agric. Eng., vol. 37, no. 1, pp. 81–94, 2020, doi: 10.21608/mjae.2020.94971.

J. D. Valiantzas, “Simplified forms for the standardized FAO-56 Penman-Monteith reference evapotranspiration using limited weather data,” J. Hydrol., vol. 505, pp. 13– 23, Nov. 2013, doi: 10.1016/J.JHYDROL.2013.09.005.

K. C. Perera, A. W. Western, B. Nawarathna, and B. George, “Comparison of hourly and daily reference crop evapotranspiration equations across seasons and climate zones in Australia,” Agric. Water Manag., vol. 148, pp. 84–96, Jan. 2015, doi: 10.1016/J.AGWAT.2014.09.016.

L. S. Pereira, R. G. Allen, M. Smith, and D. Raes, “Crop evapotranspiration estimation with FAO56: Past and future,” Agric. Water Manag., vol. 147, pp. 4–20, Jan. 2015, doi: 10.1016/J.AGWAT.2014.07.031.