Verification SEBAL and Hargreaves –Samani Models to Estimate Evapotranspiration by Lysimeter Data

Document Type : Research Article

Authors

• Ali Morshedi ¹
• Seyed Hassan Tabatabaei
• Mahdi Naderi ²

¹ Agricultural and Natural Resources Center. Shahrekord

² Shahrekord University

https://doi.org/10.22067/jsw.v30i2.39762

Abstract

Introduction: Evapotranspiration (ET) is an important component of the hydrological cycle, energy equations at the surface and water balance. ET estimation is needed in various fields of science, such as hydrology, agriculture, forestry and pasture, and water resources management. Conventional methods used to estimate evapotranspiration from point measurements. Remote sensing models have the capability to estimate ET using surface albedo, surface temperature and vegetation indices in larger scales. Surface Energy Balance Algorithm for Land (SEBAL) estimate ET at the moment of satellite path as a residual of energy balance equation for each pixel. In this study Hargreaves-Samani (HS) and SEBAL models ET compared to an alfalfa lysimeter data’s, located in Shahrekord plain within the Karun basin. Satellite imageries were based on Landsat 7 ETM+ sensor data’s in seven satellite passes for path 164 and row 38 in the World Reference System, similar to lysimeter sampling data period, from April to October 2011. SEBAL uses the energy balance equation to estimate evapotranspiration. Equation No. 1 shows the energy balance equation for an evaporative surface:
λET=Rn–G–H [1]
In this equation Rn, H, G and λET represent the net radiation flux input to the surface (W/m2), Sensible heat flux (W/m2), soil heat flux (W/m2), and latent heat of vaporization (W/m2), respectively. In this equation the vertical flux considered and the horizontal fluxes of energy are neglected. The above equation must be used for large surfaces and uniformly full cover plant area. SEBAL is provided for estimating ET, using the minimum data measured by ground equipment. This model is applied and tested in more than 30 countries with an accuracy of about 85% at field scale, and 95 percent in the daily and seasonal scales. In Borkhar watershed (East of Isfahan, IRAN) ASTER and MODIS satellite imageries were used for SEBAL to compare Penman-Monteith model. Results showed that estimated ET of SEBAL were about 20% less than sugar beet ET and about 15% more for maize ET by Penman-Monteith. He concluded the differences may be due to the limited number of satellite imageries which extrapolated ET through the entire growth period and the data obtained from the weather station far from 24 km in the studied area. In another study at Zayanderud Basin, the different irrigation networks were examined using Landsat 7 imageries to increase the spatial resolution of NOAA satellite to determine the energy balance components and actual evapotranspiration. In this study, data from a lysimeter to a depth of 2.5 m and a diameter of 3 meters planted with alfalfa in the Chahar-Takhteh agricultural research station (Agricultural and natural resources research center of Shahrekord, IRAN) was used. The lysimeter (LYS_REF) located in the in the middle of 25 × 40 m (1000 square meter) alfalfa cultivated farm, surrounded by other planted area. The lysimeter used to measure the reference evapotranspiration (ETr) and around alfalfa was used as cold pixels.
Materials and Methods: This study was conducted to evaluate SEBAL and Hargreaves-Samani estimated ET models against evapotranspiration measured by lysimeter within the Shahrekord plain. Meteorological data required for a period of 185 days (according to the lysimeter data period) includes minimum and maximum relative humidity (RHmax and RHmin), maximum and minimum air temperature (Tmax and Tmin), wind speed at two meters (U2), precipitation, evaporation rate, sunshine hours, air pressure and dew point temperature obtained from a weather station nearby lysimeter. In order to assess reference evapotranspiration (ETr) models, statistical indices such as the coefficient of determination (R2), mean absolute error (MAE), mean bias error (MBE), root mean square error (RMSE) and index of agreement (d) were used.
Results and Discussion: The results showed that RMSE, MAE and MBE for SEBAL model over the lysimeter data were 1.782, 1.275 and -0.272 mm/day and 0.700 for the d index, respectively. Similar indices for the Hargreaves-Samani model were 1.003, 0.580 and 0.290 mm/day and 0.917 for the d index. For HS model results show that RMSE, MAE and MBE values were 0.813, 0.477 and 0.206 mm/day, and 0.930 for the index of d, during the entire growing period (185 days).
Conclusion: However, results showed that the efficiency and reliability of the SEBAL model by processing satellite visible, near infrared and thermal infrared bands. The need for irrigation water requirements and ET estimation are noteworthy, during the growth of various plants, which vary and thus the complete time series of satellite imageries is required to estimate the total and annual evapotranspiration.

Keywords

• Alfalfa
• Energy Equation
• Landsat
• Shahrekord plain

20.1001.1.20084757.1395.30.2.23.5

References

1- Abdoli H., Eslamian S.S., and Abedi Koohpaei J. 2011. The use of Landsat7 satellite images and MODIS for estimating evapotranspiration through remote sensing in irrigation management. 3rd Irrigation and Drainage Network Management National Conference (IDNC201). Faculty of Irrigation Engineering, Shahid Chamran University.(In Persian)

2- Akbari M., Toomanian N., Droogers P., Bastiaanssen W.G.M., and Gieske A. 2007. Monitoring irrigation performance in Esfahan, Iran, using NOAA satellite imagery. Agricultural water management, 88, 99–109.

3- Allen R.G., Morse A., Tasumi M., Bastiaanssen W.G.M., Kramber W., and Anderson H. 2001. Evapotranspiration from Landsat (SEBAL) for water rights management and compliance with multi-state water compacts. Geoscience and Remote Sensing Symposium, (2), 830-833.

4- Allen R.G., Pereira L.D., Raes D., and Smith M. 1998. Crop evapotranspiration Guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper 5627-65.

5- Allen R. G., Tasumi, M., and Trezza R. 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-model. Journal of Irrigation and Drainage Engineering. 133, 380-394.

6- Anderson M. C., Allen R. G., Morse A., and Kustas W. P. 2012. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources: Remote Sensing of Environment. Vol. 122, 50-65.

7- Bastiaanssen W.G.M. Ahmad M. D. and Chemin Y. 2002. Satellite surveillance of evaporative depletion across the Indus Basin. Water Resource Research, 38 (12), 1273.

8- Bastiaanssen W.G.M. and Chandrapala L. 2003. Water balance variability across Sri Lanka for assessing agricultural and environ- mental water use. Agric. Water Manage, 58(2), 171–192.

9- Bastiaanssen W.G.M., and Bandara K.M.P.S. 2001. Evaporative depletion assessments for irrigated watersheds in Sri Lanka. Irrigation Science, 21, 1–15.

10- Bastiaanssen W.G.M., Noordman E.J.M., Pelgrum H., Davids G., Thoreson B.P., and Allen R.G. 2005. SEBAL model with remotely sensed data to improve water-resources management under actual field conditions. ASCE Journal of Irrigation and Draiage Engineering. 131, 85-93.

11- French A.N., Jacob M.C., Anderson W.P., Kustas W., Timmermans A., Gieske Z., Su M.F., Mc Cabe F., Li J., and Brunsell N. 2005. Surface energy balance fluxes with the Advanced Spaceborn Thermal Emmisssion and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA). Remote sensing Environ. 99(1-2): 55-65.

12- Jia Z., Liu S., Xu Z., Chen Y., and Zhu M. 2012. Validation of remotely sensed evapotranspiration over the Hai River Basin, China, Journal of Geophysical Research., 117, D13113.

13- Mahdavi A., Nouri Emamzadei M. R., Mahdavi Najafabadi R., and Tabatabaei S. H. 2011. Identification of Artificial Recharge Sites Using Fuzzy Logic in Shahrekord Basin. Journal of Water and Soil Science- Isfahan University of Technology, 15, 63-78. (In Persian)

14- Melesse A.M., Abtew W., and Dessalegne T. 2009. Evaporation Estimation of Rift Valley Lakes: Comparison of Models. Sensors, 9, 9603-9615.

15- Mobasheri M.R., Khavarian H., and Moussaoui H. 2006. Error estimates of ET from Sensible Heat in the SEBAL. National Conference on Irrigation and Drainage network management, Shahid Chamran University, Department of Water Engineering. (In Persian)

16- Mohamed Y., Bastiaanssen W.G.M., and Savenije H.H.G. 2004. Spatial variability of evaporation and moisture storage in the swamps of the upper Nile studied by remote sensing techniques. Journal of Hydrology, 277, 116–124.

17- Mohseni Saravi M., Ahmadi H., and Nosrati K. 2010. Estimation of evapotranspiration in Taleghan Basin using SEBAL. The First International Conference on Plant, Water, Soil and Weather Modeling. International center for science, high technology, environmental sciences. Shahid Bahonar University of Kerman.(In Persian)

18- Mokhtari M.H. 2005. Agricultural drought impact using remote sensing. Ms. C. Diss., ITC. The Netherlands.

19- Morshedi A. 2013. Estimation and mapping actual evapotranspiration using remote sensing data in Shahrekord plain. Ph.D. Thesis. Shahrekord University, Shahrekord, IRAN. (In Persian)

20- Noori S., Sanaei-Nejad H., and Hasheminia M. 2010. Estimation of evapotranspiration with SEBAL model using MODIS images in regional scale. The First International Conference on Plant, Water, Soil and Weather Modeling. International center for science, high technology, environmental sciences. Shahid Bahonar University of Kerman. (In Persian)

21- Sanaei Nejad S.H., Noori S., and Hasheminia S.M. 2011. Estimation of Evapotranspiration Using Satellite Image Data in Mashhad area. Journal of Water and Soil, vol. 25, No.3, 540-547. (In Persian)

22- Seif Z., Akbari A., and Zareabyaneh H. 2010. Review of the energy balance algorithm at ground level and its relationship to determine evapotranspiration in irrigation networks.The First International Conference on Plant, Water, Soil and Weather Modeling. International center for science, high technology, environmental sciences. Shahid Bahonar University of Kerman. (In Persian)

23- Singh R.K., Irmak A., Irmak S., and Martin D.L. 2008. Application of SEBAL Model for mapping Evapotranspiration and Estimating Surface Energy Fluxes in South- Central Nebraska. Journal of Irrigation and Drainage Engineering.134: 273-285.

24- Tasumi M., Allen R.G., Trezza R., and Wright J. L. 2005. Satellite-based energy balance to assess within-population variance of crop coefficient curves. American Society of Civil Engineers, Journal of Irrigation and Drainage Engineering 131(1): 94–109.

25- Wang J., Sammis, T. W., Gutschick V. P., Gebremichael M., and Miller D. R. 2009. Sensitivity Analysis of the Surface Energy Balance Algorithm for Land (SEBAL). American Society of Agriculture and Biological Engineering. Vol. 52 (3):801-811.

26- Weiqiang Ma., Hafeez M., Rabbani U., Ishikawa H., and Ma Y. 2012. Retrieved actual ET using SEBS model from Landsat- 5T Mdata for irrigation area of Australia. Atmospheric Environment 59, 408-414.

27- Zareabayneh H., Bayat Varkeshi M., Sabziparvar A. A., Marofi S., and Ghasemi A. 2011. Evaluation of Different Reference Evapotranspiration Methods and their Zonings in Iran. Physical Geography Research Quarterly, 42(4), 95-109.(In Persian)

28- Zhongping S., Wei Sub W., Shen W., Wang C., You D., and Liu Z. 2012. Evapotranspiration estimation based on the SEBAL model in the Nansi Lake Wetland of China. Mathematical and Computer Modelling 54, 1086–1092.