Spatial variability of evapotranspiration regards to extreme temperatures using remote sensing data in Iran

Document Type : Original Research

Authors
R. Ghareloo 1
E. ghasemifar 2
1 no conflict
2 Tarbiat Modares University
Abstract
Introduction: According to the Intergovernmental Panel on Climate Change (IPCC) in 2012, globally, a large number of climatic events have increased in recent decades such as extreme temperatures, floods and etc. That’s the number of warm days and nights has increased, and climate models predict extreme temperature by the end of the 21st century (IPCC, 2012). Ecosystems, the global economy and public health are highly vulnerable to these extreme events, especially extreme temperatures (Kunkel et al., 1999). Generally, in Iran, the regionalization of extreme temperatures has been studied. For example, Rezaei et al. (2015) examined the extreme temperatures in two months with extreme temperature and identified different areas for Iran. Masoudian and Darand(2008) also studied extreme cold temperature in Iran and regionalized six areas for Iran. Considering the studies that indicates the occurrence of extreme temperatures for different parts of the world, it is interesting to note the role of these extreme temperatures on evapotranspiration difference between extreme cold and warm temperatures. Evapotranspiration is the water loss from the ground to the atmosphere and defined as a key process in the water cycle (Wang and Dikeson, 2012), which is related to plant growth (Alberto et al., 2014), drought (Anderson et al, 2011), greenhouse gas (Balogh et al., 2015) and climate change (Abtew and Melesse, 2012). The purpose of this study is to answer the question of what is the changes in evapotranspiration under extreme temperature conditions in Iran.

Methodology: For answer the research’s question it found clearly that January 2008 and July 2010 had recorded extreme cold and warm temperatures during the period of 30 years. For these two months, 55- air temperature stations data, soil temperature from NCEP / NCAR reanalysis database, land surface temperature (LST), vegetation cover, and evapotranspiration from Moderate Resolution Imaging Spectroradiometer (MODIS) were utilized in five kilometer or 0.05 degree resolution. At first, the risk of occurrence of the extreme temperatures was determined by the distribution of the cumulative risk and the Gumbel distribution during these two months. The land surface temperature data product (LST) namely MOD11C3, which has 0.05 degrees (approximately 5 kilometers or 5600 meters) and with a monthly and global time scale was used. To investigate the changes in evapotranspiration, the MODIS evapotranspiration product namely MOD16 was utilized (Mu et al., 2012). The data is available on an annual, eight-day and monthly basis. In this process, evapotranspiration is provided globally and with a resolution of one kilometer covering 109 million square kilometers of the land’s surface. The algorithm used the Penman-Monteith equation to produce this product (Monteith, 1965). Then for the analysis Pearson’s correlation and coefficient of determination were used.

Results and discussion: The results showed that the occurrence of extreme temperatures above 50 degrees Celsius is 0.06 in July and temperatures higher than 22 degrees Celsius is 0.008 in January. Also, the probability of temperature higher than 5 degrees Celsius is 0.50 in January. Correlations results indicated that the two factors of energy (air temperature) and soil moisture are the main controller of the relationship between these parameters (LST and evapotranspiration), so that when the air temperature was above 5 degrees Celsius, a significant negative correlation was observed (-0.24 in January and -0.64 In July) and when the air temperature is below than 5 degrees, it will be positive (0.23 in January). Generally, regardless of the threshold, a negative correlation was obtained for every two months, but a weakest negative correlation (close to zero) was observed in January, due to the recording of temperatures exceeding 5 ° C with an incidence of 50%. The humidity factor shows that every two months have suffered from a certain moisture threshold due to extreme cold and warm temperatures, and if there is a moisture limit, this relation will be negative, thus it’s a determination factor for the overall negative relationship (regardless of the temperature threshold) in January.

Conclusion: The extreme temperatures showed the highest impact on evapotranspiration so that air temperature was identified as a trigger for the relationship between LST and evapotranspiration.


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1. Abtew,W.,Melesse, A, 2012. Evaporation and Evatranspiration ,Chapter Climate Change and Evapotranspiration, Springer.DOI:10.1007/978-94-007-4737-1_13.
2. Afrasiabi Gorgani S , Panahi M and Rezaie F, 2013. The Relationship between NDVI and LST in the urban area of Mashhad, Iran, International Conference on Civil Engineering Architecture & Urban Sustainable Development 27&28 November 2013, Tabriz , Iran
3. Alberto M. C. R., Quilty J. R., Buresh R. J. et al.,2014. “Actual evapotranspiration and dual crop coefficients for dry-seeded rice and hybrid maize grown with overhead sprinkler irrigation,” Agricultural Water Management, vol. 136, pp. 1–12, 2014.View at Publisher • View at Google Scholar • View at Scopus.
4. Alexander, L. V. and Coauthors ,2006. Global observed changes in daily climate extremes of temperature and precipitation, J. Geophys. Res. Atmos., 111, D05109, doi: 10.1029/2005JD006290.
5. Anderson MC, C. Hain, B. Wardlow, A. Pimstein, J. R. Mecikalski, and W. P. Kustas,2011. “Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States,” Journal of Climate, vol. 24, no. 8, pp. 2025–2044, 2011. View at Publisher • View at Google Scholar • View at Scopus
6. Balogh J., S. Fóti, K. Pintér et al.,2015. “Soil CO2 efflux and production rates as influenced by evapotranspiration in a dry grassland,” Plant and Soil, vol. 388, no. 1-2, pp. 157–173, 2015. View at Publisher • View at Google Scholar • View at Scopus
7. Donat MG., Alexander LV.,2012. The shifting probability distribution of global daytime and night-time temperatures,Geophysic research,lett39,14, l14707.DOI: 10.1029/2012GL052459.
8. El Kenawy A, López-Moreno JI and Vicente-Serrano SM, 2013. Summer Temperature Extremes in Northeastern Spain: Spatial Regionalization and Links to Atmospheric Circulation (1960-2006), Theoretical and Applied Climatology, 113: pp.387-405.
9. Haynes, J.V., Senay,G.B, 2012. Evaluation of the Relation between Evapotranspiration and Normalized Difference Vegetation Index for Downscaling the Simplified Surface Energy Balance Model, Scientific Investigations Report 2012–5197.
10. Huete, A. J., 1999. MODIS VEGETATION INDEX (MOD 13), ALGORITHM THEORETICAL BASIS DOCUMENT. Version 3, 2University of Virginia Department of Environmental Sciences Clark Hall Charlottesville,.
11. Hope, A., Engstrom R., and Stow, D, 2005. “Relationship between AVHRR surface temperature and NDVI in Arctic tundra ecosystems,” International Journal of Remote Sensing, vol. 26, no. 8, pp. 1771–1776, 2005.View at Publisher • View at Google Scholar • View at Scopus
12. IPCC, 2012. Summary for Policymakers. In: Field, C.B., Barros, V., Stocker, T.F., Qin, D., Dokken, D., Ebi, K.L., Mastrandrea, M.D., Mach, K.J., Plattner, G.-K., Allen, S.K., Tignor, M., Midgley, P.M. (Eds.), Intergovernmental Panel on Climate Change Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
13. Jakob,D., Walland,D, 2016. Variability and long-term change in Australian temperature and precipitation extremes, Weather and Climate Extremes 14:36–55.
14. Kosa,P.,2009. Air Temperature and Actual Evapotranspiration Correlation Using Landsat 5 TM Satellite Imagery, Kasetsart J. (Nat. Sci.) 43 : 605 – 611.
15. Kunkel,K E., Andsager ,K,1999. Long-Term Trends in Extreme Precipitation Events over the Conterminous United States and Canada,J climte,12,2515-2527. DOI: http://dx.doi.org/10.1175/1520-0442(1999)012<2515:LTTIEP>2.0.CO;2
16. Monteith, J. L. 1965, Evaporation and environment. In: B.D. Fogg, (Ed.), The State and Movement of Water in Living Organism, Symposium of the society of experimental biology, 19, Cambridge University Press, Cambridge (1965), 205–234.
17. Mu, Q., Zhao, M., and Running, S. W.: Brief Introduction to MODIS Evapotranspiration Data Set (MOD16), available at: ftp://ftp.ntsg.umt.edu/pub/MODIS/NTSG_Products/MOD16/ MOD16A3.105_MERRAGMAO/Geotiff (last access: July 2014), 2012. Segal, M., Avissar, R., McCumber, M. C., Pielke, R. A., 1988. Evaluation of vegetation effects on the generation and modification of mesoscale circulations, J. Atmos. Sci., 45,pp. 2268– 2293.
18. Stull, R. B, 1988. An introduction to boundary layer meteorology, Kluwer academic publications.
19. Sun,Z.,Wang,Q., Batkhishig,O., and Ouyang,Z, 2016. Relationship between Evapotranspiration and Land Surface Temperature under Energy- and Water-Limited Conditions in Dry and Cold Climates, Volume 2016 (2016), Article ID 1835487, 9 pages,http://dx.doi.org/10.1155/2016/1835487.
20 Wan, Z. and Z.-L. Li, 1997.‘‘A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data,’’ IEEE Trans. Geosci. Remote Sens., vol. 35, no. 4, pp. 980-996.
21. Wang K. and R. E, 2012.Dickinson, “A review of global terrestrial evapotranspiration: observation, modeling, climatology, and climatic variability,” Reviews of Geophysics, vol. 50, no. 2, pp. 1–54, View at Publisher • View at Google Scholar • View at Scopus
22. Zhang,Y., Gao,Z, Li,D.,Huang, X, 2017. Spatiotemporal variability of extreme temperature frequency and amplitude in China, Atmospheric ResearchVolume 185, 1 March 2017, Pages 131–141
The Journal of Spatial Planning and Geomatics
Volume 23, Issue 4
December 2019
Pages 193-212