Analysis of Temporal Change of Actual Evapotranspiration and Its Relationship with Temperature and Precipitation in East Azarbayejan Province Using MODIS

Document Type : Full length article


1 PhD Candidate in Climatology, Physical Geography Department, University of Tabriz, Iran

2 Professor of Climatology, Physical Geography Department, University of Tabriz, Iran

3 Associate Professor of Climatology, Physical Geography Department, University of Tabriz, Iran

4 Professor of Climatology, Physical Geography Department, University of Esfahan, Iran


The increasing expansion of industries and the use of fossil fuels have led to an increase in greenhouse gas emissions and, consequently, global warming and the occurrence of climate change phenomena. Climate change is a change in the climate behavior of an area relative to the behavior that is expected during a long-term period of observed or recorded information in that area. The ET element is considered as one of the most important components of water balance in nature; it is strongly influenced by important climate components such as temperature and under the influence of climate change, it can exhibit various reactions. Therefore, in calculating the water balance of each basin, ET calculation is important. Because ET along with the surface flow and water penetration in the soil are considered as components of the water balance. Due to the temporal variations of climate variables and consequently ET, the use of remote sensing methods can consider these changes as more favorable. Using Landsat satellite imagery and energy balance model, using the NOAA, AVHRR and SEBS algorithms, Baba Jafari et al (2015) estimated monthly ETA values for agricultural use in the Akhole region in Tabriz. Comparison of the results of the algorithm with observation values ​​indicates the accuracy of the model with a coefficient of 0.8 and the mean square root error of 9.64 millimeter per month (Baba Jafari et al., 2015: 1).
Materials and methods
In this study, for the analysis of the ETa of the East Azarbaijan province, we applied MOD16A2 remote sensing data in a time interval of 8 days in the period 2014-2000. Data on maximum, minimum and precipitation temperatures of 11 stations of the province were also used for the purpose. Since the relationship between temperature and ETa is linear, the correlation between the average of the maximum and minimum temperatures of 8 days in the stations of the province was calculated with the mean values ​​of 8 daily ETa for representative cells. As the purpose of the current research is to estimate ETa for explaining the water balance of the province, the correlation between precipitations of the stations in the province with the ETa of the representative cells was also evaluated. Given the fact that the data of the MODIS are 8 daily scales, with programming in MATLAB software, the data of the maximum, minimum and precipitation temperatures of the stations was converted into eight days interval. Investigations showed that MODIS provides only ETa data of the representative cells of Ahar, Jolfa, Sarab, Sahand, Marand and Meyaneh stations, and for other stations due to the placement of the cell in the class of ground cover construction, ETa data has not been recorded.
Results and discussion   
The findings of this study indicate that the average maximum and minimum mean and total mean of ETa data on 124546 pixels in the province for the period from 2000 to 2014 were 2.3, 0.5 and 0.8 millimeter per day. The time series ETa of the cells within the boundaries of the province also showed the highest changes in 2003 and 2010, at 9.1 millimeter a day, and the lowest in 2001, at 0.1 millimeter per day. In this regard, the time series of the ETa of the stations in the study years showed that the highest and lowest values ​​of ETa changes were in the Sarab and Sahand stations, and the value was 3.3 (millimeters per day) in 2012 and 0.01(millimeters per day) in 2001. Due to the fact that the temperature relationship with ETa is linear, the correlation between the average maximum and minimum temperatures of 8 days in the stations of the province with average ETa values ​​of 8 days for representative cells of the station was calculated. Investigations showed that there was a negative relationship between the mentioned parameters. Basically, there must be a positive and direct relationship between temperature and ETa. The only factor that can disrupt this relationship is the lack of adequate water in the area. Regression analysis on min and max temperature data matrix with ETa of 8 days in the stations at 95% confidence level showed that with 1 degree increase in temperature in the province, the ETa decreases by 0.02 mm per day. The ETa variation slope in relation to precipitation stations showed that at 95% of confidence level, with an increase of 1 millimeter of precipitation, the ETa would increase by 0.9 millimeter per day.
In this study, was investigated the correlation between the average maximum and minimum temperature of ground stations with the mean values of 8 daily ETa of the representative cells of the stations. The results of the investigations showed that there is a negative relationship between the mentioned parameters. The existence of a negative relationship between the above mentioned parameters reflects the fact that when the warm season approaches, the water resources are decreasing as a result of decreasing water, and also ETa values ​​decrease with increasing temperature. Because there is not enough water to evaporate in the area, the correlation between mean precipitation and ETa also showed a positive relationship. In other words, the higher the rainfall, the higher is the water available to the plant and the soil. Given that the average annual rainfall in the province is 267.27 mm and the annual loss of ETa is 32.4 mm. This amount of evaporation is significant compared with 267.27 millimeters of annual rainfall. The climate condition of the province leads to desert and dry land.


Main Subjects

Allen, R.G.; Tasumi, M. and Trezza, R. (2000). Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)-Model, ASCE Journal of Irrigation and Drainage Engineering, 133(4): 380-394.
BabaJafari, H.; Pymozd, Sh.; Moghaddasi, M. and Baghri, M. (2015). Evaluation of SEBS Algorithm in Estimation of Real Evapotranspiration Based on Images of AVHRR of NOAA Satellite (Case Study: Tabriz Plain), Journal of Applied Research in Water Sciences, 2: 1-10 (In Persian).
Folhes, M.T.; Renno, C.D. and Soares, J.V. (2009). Remote sensing for irrigation water management in the semi-arid Northeast of Brazil, Agricultural Water Management, 96(10): 1398-1408.
George, P.; Prasanna, H.G.; P. V., Vara Prasad; Terry, A. H, Scott A. S. (2013). Lysimetric evaluation of SEBAL using high resolution airborne imagery from BEAREX08, Advances in Water Resources, in press.
Goyal, R.K. (2004). Sensitivity of evapotranspiration to global warming: A case study of arid zone of Rajasthan (India), Agricultural Water Management, 69(1): 1-11.
Jafarpoor, A. (2002). Principles of Climatology, Third Edition, Payame Noor University Press, Tehran (In Persian).
Jia, D.; Kaishan, S.; Zongming, W.; Bai, Z. and Dianwei, L. (2013). Evapotranspiration estimation based on MODIS products and surface energy balance algorithms for land (SEBAL) model in sanjiang plain northeast china, China Geographical Science, 23(1): 73-91.
Karamooz, M. and Araghinejad, SH. (2002). Advanced Hydrology, Second Edition, Imam Reza University Press (In Persian).
 Karimi, A.; Bansoleh, B.F. and Hesari, H. (2012). Estimate of real evapotranspiration on a regional scale using the Sabal algorithm and Landsat images, Irrigation and Drainage Journal of Iran, 4: 353-364 (In Persian).
Kaviani, A.; Sohrabi, T. and Arasteh, P. (2011). Evapotranspiration and water productivity estimation using SEBAL algorithm and comparison with lysimeter data, Iranian Journal of Irrigation and Drainage, 1(2): 161-171 (In Persian).
Levinson, D.H. and Fettig, C.J. (2014). Climate change: Overview of data sources, observed and predicted temperature changes, and impacts on public and environmental health, Climate Change and Global Public Health, New York: Springer, pp. 31-47.
Mischke, S.; Aichner, B.; Diekmann, B.; Herzschuh, U.; Plessen, B.; Wünnemann, B. and Zhang, C. (2010). Ostracods and stable isotopes of a late glacial and Holocene lake record from the NE Tibetan Plateau, Chemical Geology, 276(1-2): 95-103.
Moradi, F.; Kamali, GH. and Vazifeh Doost, A. (2015). Estimation of crop potentiol
Evapotranspiration Based on MODIS Products by using of statistics of the synoptic stations in Zanjan, Climatological studies, 23(24): 39-49.
Mu, Q.; Heinsch, F.A.; Zhao, M. and Running, S.W. (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data, Remote Sensing of Environment, 111: 519-536.
Omidvar, J.; Davari, K.; Arshad, S.; Moosavi Bybaigi, M.; Akbari, M. and Hosseini, A.F. (2012). Estimation of Actual Evapotranspiration by Using Aster and Metric Models, Journal of Irrigation Engineering and Water Engineering, 9: 38-49 (In Persian).
Pakparvar, M.; Cornelis, W.; Pereira, L.S.; Gabriels, D.; Edraki, M.; Hafeez, M. and Kowsar S.A. (2014). Remote sensing estimation of actual evapotranspiration and crop coefficients for a multiple land use arid landscape of southern Iran with limited available data, Journal of Hydroinfomatics, 16(6): 1441-1460.
Pirmoradian, N.; SHamsnia, A. and Shahrokhnia, M. A. (1999). Monitoring and analysis of spatial distribution of drought severity in 1379-1380 cropping season in Fars province using the Standard of Precipitation Index (SPI) in the geographic information system environment, Journal of Water Resources Engineering Research, 1: 61-70 (In Persian).
Rezaiebanafsheh, M.; Jahanbakhsh, S. and Valizadeh, KH. (2013). Estimate of real evapotranspiration in the White River Basin using satellite imagery processing,
Geographical Space Scientific-Research Quarterly, 44: 241-262 (In Persian).
Sanaienejad, H.; Nori, S. and Hasheminia, M. (2011). Estimation of Real Evapotranspiration Using Satellite Images in Mashhad Region, Water and Soil Journal, 3: 540-547 (In Persian).
Sun, Y.J.; Wang, J.F.; Zhang, R.H.; Gillies, R.R.; Xue, Y. and Bo, Y.C. (2005). Air temperature retrieval from remote sensing data based on thermodynamics, Theoretical and Appl. Climatol, 80(1): 37-48.
Sun, Z.; Wei, B.; Su, W.; Shen, W.; Wang, C.; You, D. and Liu, Z. (2011). Evapotranspiration estimationbased on the SEBAL model in the Nansi Lake wetland of china, Mathematical and computer modeling in agriculture, 54(3): 1086-1092.
Weatherhead, E.C.; Stevermer, A.J. and Schwartz, B.E. (2002). Detecting environmental changes and trends, Physics and Chemistry of the Earth, Parts A/B/C. 27(6-8): 399-403.
Yaghobzadeh, M.; Boromandnasab, S.; Ezadpanah, Z. and Sayedkaboli, H. (2015). Investigation of the spatial and temporal changes of evapotranspiration by means of remote sensing in semi-arid regions, Journal of Water Research in Agriculture, 2: 221-234 (In Persian).
Volume 50, Issue 4
January 2019
Pages 685-696
  • Receive Date: 18 December 2017
  • Revise Date: 28 August 2018
  • Accept Date: 28 August 2018
  • First Publish Date: 22 December 2018