Document Type : Research Paper

Authors

Abstract

The SEBAL algorithm is used to estimate spatial distribution of actual evapotranspiration using remote sensing imageries of MODIS or Landsat. Despite having a better spatial resolution than MODIS imageries (30 m instead of 1000 m), Landsat imageries do not have an appropriate temporal resolution (every 16 days instead of daily). On the other hand, the daily imageries of MODIS can be difficult to use under cloudy condition. Additionally, it is also a time-consuming process to interpret all the imageries. In this research, we chose the appropriate imageries from MODIS to be able to monitor the sudden weather changes as well as rainfall events and to reduce the interpretation time, while keeping the important information of the daily MODIS imageries in order to obtain the better actual evapotranspiration estimates. Due to the importance of hot and cold pixel selection, whose selection needs time and proficiency, we applied the automated method of hot and cold pixel selection (without user-intervention) using Landsat imageries. To integrate evapotranspiration over time, we used linear-logarithmic interpolation method in addition to linear interpolation method. The estimated actual evapotranspiration by SEBAL was compared to the estimated actual evapotranspiration from water balance equation and SWAT model for 3 years including a wet year (2004-2005), a normal year (2005-2006) and a dry year (2007-2008) in the Neishaboor-Rokh watershed. Furthermore, we used the Budyko framework to validate the evapotranspiration estimated by SEBAL and SWAT. The results showed that in comparison to SWAT, the linear-logarithmic interpolation method performed better than the linear method to estimate evapotranspiration. For linear-logarithmic method, RMSE, MBE, and MAE were 20.4, 0.09, and 18.4 mm year-1, respectively; and for the linear method, they were 21.8, -2.4, and 20.8 mm year-1, respectively. The results also demonstrated that the SEBAL algorithm with automated cold and hot pixel selection is able to have a good estimate of actual evapotranspiration at annual time scale and watershed scale. But, the algorithm does not perform well at HRUs and monthly scale in comparison to SWAT model. Results showed that by taking irrigation into account, evaporation estimated by SEBAL and SWAT follows the Budyko framework.

Keywords

  1. آزاد مرزآبادی، م.ر. 1393. مقایسه الگوریتم­هایSEBAL و S-SEBI در برآورد تبخیر – تعرق در دشت نیشابور با استفاده از تصاویر سنجندهMODIS. پایان نامه کارشناسی ارشد گروه مهندسی آب. دانشکده کشاورزی. دانشگاه فردوسی مشهد.
  2. ایزدی، ع.ا. 1392. کاربرد و ارزیابی یک مدل توسعه یافته تلفیقی آب زیرزمینی- آب سطحی در حوضه آبریز نیشابور. رساله دکتری گروه مهندسی آب. دانشکده کشاورزی. دانشگاه فردوسی مشهد.
  3. شرکت آب منطقه­ای خراسان رضوی. 1377. گزارش محاسبه بیلان منابع آب حوضه آبریز نیشابور.
  4. موذن زاده، ر. 1392. پایش سیستم هیدرولوژیک حوضه آبریز نیشابور با استفاده از تکنیک سنجش از دور. رساله دکتری. گروه مهندسی آب. دانشکده کشاورزی. دانشگاه فردوسی مشهد.
  5. مهندسین مشاورساز آب شرق.1387.مطالعات بهم پیوسته منابع آب حوضه نیشابور.گزارش­های هواشناسی، هیدرولوژی و خاکشناسی. مشهد.
  6. یاوری، م. 1392. ارزیابی روش­های تجربی برآورد تبخیر- تعرق واقعی سالانه در مقیاس بزرگ به کمک تبخیر- تعرق برآوردی از روش سبال در دشت نیشابور. پایان نامه کارشناسی ارشد گروه مهندسی آب. دانشکده کشاورزی. دانشگاه فردوسی مشهد.

 

  1. Allen, R.G., B. Burnett, W. Kramber, J. Huntington, J. Kjaersgaard, A. Kilic, C. Kelly and R. Trezza. 2013. Automated calibration of the METRIC-Landsat evapotranspiration process. J. Am. Water Resour. Assoc. 49(3): 563–576.
  2. Allen, R.G., M. Tasumi  and R. Trezza. 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Applications. J. Irrig. Drain. Eng 133(4): 395–406.
  3. Allen, R. Morse, A. Tasumi, M. 2003.”Application of SEBAL for western US water rights regulation and planning”. ICID workshop on remote sensing of ET for large regions.
  4. Arnold, J.G., R. Srinivasan, R.S. Muttiah and J.R. Williams. 1998.  Large area hydrologic modeling and assessment, part 1: model development. J Am Water Resour Assoc (JAWRA) 34:73–89
  5. Arora, V.K. 2002. The use of the aridity index to assess climate change effect on annual runoff. J. Hydrol. 265(1-4): 164–177.
  6. Budyko, M. I. 1974. Climate and Life. Academic Press. Orlando, Florida. 508 pp.
  7. Bastiaanssen, W.G.M., E.J.M.Noordman, H. Pelgrum, G. Davids, B.P. Thoreson, and R.G. Allen. 2005. SEBAL model with remotely sensed data to improve water-resources management under actual field condition. J. Irrig. Drain Eng. 131( 1): 85-93.
  8. Bastiaanssen, W.G.M. 2000. SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. J. Hydrol. 229(1-2): 87–100.
  9. Bastiaanssen, W.G.M., M. Menenti, R.A. Feddes, and A.A.M. Holtslag. 1998a. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol. 212-213(1-4): 198–212.
  10. Bastiaanssen, W.G.M., H. Pelgrum, J. Wang, Y. Ma, J.F. Moreno, G.J. Roerink, and T. van der Wal. 1998b. A remote sensing surface energy balance algorithm for land (SEBAL). 2. Validation. J. Hydrol. 212-213: 213–229.
  11. Fu, B.P. 1981. On the calculation of the evaporation from land surface. Scientia Atmospherica Sinica. 5: 23–31.
  12. Gao, Y., and D. Long. 2008. Intercomparison of remote sensing-based models for estimation of evapotranspiration and accuracy assessment based on SWAT. Hydrol. Process.  22(25): 4850–4869.
  13. Han, S., H. Hu, D. Yang, and Q. Liu. 2011. Irrigation impact on annual water balance of the oases in Tarim Basin, Northwest China. Hydrol. Process. 25(2): 167–174.
  14. Izady, A., K. Davary, A. Alizadeh, A. N. Ziaei, S. Akhavan,  A. Alipoor, A. Joodavi, and M.L. Brusseau. 2015. Groundwater conceptualization and modeling using distributed SWAT-based recharge for the semi-arid agricultural Neishaboor plain, Iran. Hydrogeol. J. 23(1): 47–68.
  15. Kite, G.W., and P. Droogers. 2000. Comparing evapotranspiration estimates from satellites, hydrological models and field data. J. Hydrol. 229(1-2): 3–18.
  16. Kustas, W.P., and J.M. Norman. 1999. Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agric. For. Meteorol. 94(1): 13–29.
  17. Liang, S. 2005. Quantitative Remote Sensing of Land Surfaces. John Wiley and Sons, Inc, pp 449.
  18. Menenti, M., and B. Choudhury. 1993. Parameterization of land surface evaporation by means of location dependent potential evaporation and surface temperature range. Proceding, (212): 561–568.
  19. McKee T.B., N.J. Doesken, and J. Kleist. 1995. Drought Monitoring with Multiple Time Scales. Proceeding of the Ninth Conference on Applied Climatology. American Meteorological Society: Boston.
  20. Moene, A.F., D. Schüttemeyer, and H.A.R. De Bruin. 2007. Basin-wide, year-round estimation of actual evaporation for the Volta Basin using remote sensing.Geophysical Research Abstracts of the European Geosciences Union General Assembly, 15-20 April 2007, Vienna, Austria.
  21. Neitsch, S.L., J.G. Arnold, J.R. Kiniry, J.R. Williams, and K.W. King. 2009. Soil and Water Assessment Tool. In: Theoretical Documentation: Version 2009. TWRI TR-191, College Station, TX.
  22. Norman, J.M., W. P. Kustas, and K. S. Humes. 1995. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agric. For. Meteorol. 77(3-4): 263–293.
  23. Ol’dekop, E.M. 1911. On evaporation from the surface of river basins.Trans. Meteorol. Obs. 4, 200.
  24. Pike, J.G. 1964. The estimation of annual runoff from meteorological data in a tropical climate. J. Hydrol. 2: 116– 123.
  25. Qiu, G.Y., and J. Ben-Asher. 2010. Experimental Determination of Soil Evaporation Stages with Soil Surface Temperature. Soil Sci. Soc. Am. J.  74(1): 13-22.
  26. Roerink, G.J., Z. Su, and M. Menenti. 2000. S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance. P&S. Chem .Earth (B). 25(2): 147–157.
  27. Ruhoff, A., A. Paz, W. Collischonn, L. Aragao, H. Rocha, Y. Malhi. 2012. A MODIS-based energybalance to estimate evapotranspiration for clear-sky days in Brazilian tropical savannas. RemoteSens.  4: 703–725
  28. Schreiber, P. 1904. About the relationship between the precipitation and the water management of the river in Central Europe. Z. Meteorol.21(10): 441– 452.
  29. Shuttleworth, W.J., and J.S. Wallace. 1985. Evaporation from sparse crops-an energy combination theory. Q. J. R. Meteorol. Soc.  111(469): 839–855.
  30. Su, Z. 2002. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Syst. Sci. 6(1): 85–100.
  31. Tasumi, M., R.G. Allen, and R. Trezza. 2008. At-surface albedo from Landsat and MODIS satellites for use in energy balance studies of evapotranspiration. J. Hydrol. Eng. 13: 51–63.
  32. Trezza, R., R.G. Allen, and M. Tasumi. 2013. Estimation of actual evapotranspiration along the Middle Rio Grande of New Mexico using MODIS and landsat imagery with the METRIC model. Remote Sens.  5(10): 5397–5423.
  33. Waters, R., R.G. Allen, M. Tasumi, R. Trezza, W. Bastiaanssen. 2002. SEBAL, Surface Energy Balance Algorithms for Land, Advanced Training and Users Manual, Version 1, the Idaho Department of Water Resources.
  34. Yang, Y., Shang, S., Jiang, L. 2012. Remote sensing temporal and spatial patterns of evapotranspiration
    and the responses to water management in a large irrigation district of North China. Agric. For. Meteorol. 164: 112–122.
  35. Zhao, S., Y. Yang, G. Qiu, Q. Qin, Y. Yao, Y. Xiong, and C. Li. 2010. Remote detection of bare soil moisture using a surface-temperature-based soil evaporation transfer coefficient. Int. J. Appl. Earth Obs. Geoinf.  12(5): 351–358.