عنوان مقاله [English]
Evaporation from the lakes behind the dams and freshwater lakes is one of the most important parameters in water resource losses. Therefore, zoning evaporation is very important in water resources management. There are various methods for measuring and estimating evapotranspiration (ET) that are used according to their accuracy and cost. These models include using evaporation pan and using meteorological models such as Bowen ratio and eddy correlation methods. Nowadays, modern methods such as utilizing satellite remote sensing images are developed to estimate evapotranspiration in different regions. Surface Energy Balance Algorithm for Land (SEBAL) is one of these algorithms which is highly regarded. SEBAL estimates ET as a residual in energy flux in the surface of land. This is done by calculating the difference in the soil heat flux and the sensible heat flux from the net radiation.
Material and Methods
Initially, SEBAL was extensively used for mapping evapotranspiration in lands and vegetation cover such as crop field, but it was rarely used to estimate evaporation from water bodies, especially freshwater bodies. The aim of this study was to use SEBAL algorithm to map evaporation from freshwater of dam lakes. In this regard, three Landsat Thematic Mapper images (TM) acquired in May 29th, July 9th, and August 1st, 2011 were provided from Amir Kabir (Karaj) dam and the downstream agricultural lands. The selected area is located between two provinces of Tehran and Karaj. Amir Kabir dam supplies a part of drinking and agriculture water for these two provinces. To implement SEBAL method, radiometric and geometric corrections were performed and image bands were converted to radiance and reflectance values according to their wavelengths.
In order to prepare required parameters of SEBAL, meteorological data measured by Karaj meteorological station at the selected dates were provided. These data included elevation of meteorological station, daily solar radiation, dew point temperature, wind speed, and air temperature. Furthermore, the evaporation values recorded by Tehran Regional Water Authority located at the southwestern part of the dam using evaporation pan were used as ground truth data which measurements and were performed in related dates.
All of the implementations of this research were accomplished using the ENVI 5.1, Excel 2007, Ref ET, and MATLAB R2012a software. The instantaneous ET at the time of satellite overpass was calculated calculate using SEBAL method and in order to calculate the daily ET, the calculation of the reference ET was necessary. To compute reference ET in this research, Ref ET software developed in Idaho University was utilized. One of the required data in the Ref ET software is hourly solar radiation. Because of the lack of hourly solar radiation data in the selected meteorological stations, angstrom method was used. Then, the ratio of instantaneous ET to reference ET was used to calculate the 24 hour ET.
Results and Discussion
In each stage of SEBAL method, its components, such as Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST) were checked to prevent the undesired errors. After performing implementations, the results showed that the absolute difference values of evaporation acquired from SEBAL method and ground truth data were 0.2, 0.2, and 0.3, in selected days, respectively, which leads to the Root Mean Square Value (RMSE) of 0.27 mm that is an acceptable value. Moreover, the total evaporation values from the entire dam surface for selected days were 8.037, 10.634, and 5.435 meters per day, respectively, which were significant values. The acquired map of evaporation from dam lake surface illustrated that the value of evaporation was increasing from coastal areas to deep regions. This was proved in the same researches. The values changed from 0.5 mm in the shoreline to 3.5 mm in deep water.
One of the uncertainty sources of this research was the use of daily solar radiation due to the lack of meteorological data and computing hourly values by using Angstrom method. Another uncertainty source is utilizing empirical relations such as relation of estimating atmospheric transmissivity, and narrow band emissivity and broad band emissivity which is better to calibrate before use. Another shortage of SEBAL method which can lead to uncertainty is empirical process of selecting cold and hot pixels which are used to estimate air temperature and soil temperature, respectively.
Despite the mentioned shortages of SEBAL method, the results showed the applicability of SEBAL method to mapping and estimating evaporation from freshwater lakes and dam lakes as an accurate and inexpensive method. Furthermore, the evaporation from freshwater lake surface was calculated using SEBAL model which were significant values in selected days and proved the requirement for protective operations.
1. ابراهیمی، ح.، و یزدانی، و. (1392). محاسبه تبخیر و تعرق فضای سبز به روش سبال (مطالعه موردی: پارک ملت مشهد). نشریه پژوهشهای حفاظت آب و خاک، 20 (3)، 151-133.
2. پورمحمدی، س.، دستورانی، م.، مختاری، م. ح.، و رحیمیان، م. ح. (1389). تعیین و پهنه بندی تبخیر و تعرق واقعی توسط تکنیک سنجش از دور و الگوریتم سبال (مطالعه موردی: حوضه آبخیز منشاد در استان یزد). نشریه آبخیزداری ایران، 4(13)، 32-23.
3. ثنایی نژاد، ح.، نوری، س.، و هاشمی نیا، م. (1390). برآورد تبخیر و تعرق واقعی با استفاده از تصاویر ماهواره ای در منطقه مشهد. نشریه آب و خاک، 25(3)، 547-540.
صفوی، ح. ر. (1393). هیدرولوژی مهندسی. انتشارات ارکان دانش، چاپ چهارم (اصفهان)، 724ص.
4. کاویانی, م., ع. کاویانی, and م. طاهری, کاربرد الگوریتم SEBAL در تخمین تبخیر و تعرق واقعی در دشت قزوین و مقایسه نتایج آن با داده های لایسیمتر, in دوازدهمین همایش سراسری آبیاری و کاهش تبخیر. 1392، دانشگاه شهید باهنر کرمان.
5. کریمی، ع.، مسعودی، س.، لیاقت، ع.، و فرهادی بانسوله، ب. (1390). برآورد تبخیر و تعرق واقعی در مقیاس منطقهای با استفاده از تصاویر لندست. اولین کنفرانس ملی هواشناسی و مدیریت آب کشاورزی.
6. سایت سازمان هواشناسی ایران (1395). http://www.irimo.ir.
7. 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.
8. Allen, R., Waters, R., Tasumi, M., Trezza, R., and Bastiaanssen, W. (2002). SEBAL, Surface energy balance algorithms for land, Idaho Implementation. Advanced Training and Users Manual, version 1.0.
9. Bastiaanssen, W., Menenti, M., Feddes, R., and Holtslag, A. (1998). A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. Journal of Hydrology, 212, 198-212.
10. Bastiaanssen, W. (2000). SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of hydrology, 229, 87-100.
11. Bastiaanssen, W., Noordman, E., Pelgrum, H., Davids, G., Thoreson, B., and Allen, R. (2005). SEBAL model with remotely sensed data to improve water-resources management under actual field conditions. Journal of irrigation and drainage engineering, 131(1), 85-93.
12. Bastiaanssen, W., Plegruma, H., Wang, J., Ma, Y., Moreno, J. F., Roerinka, G. J., and van der Wal, T. (1998). A remote sensing surface energy balance algorithm for land (SEBAL). 2.Validation. Journal of Hydrology, 212, 213-229.
13. De Lima, J. A. S., and Santos, J. (1995). Generalized Stefan-Boltzmann Law. International Journal of Theoretical Physics, 34 (1), 127-34.
14. Duffie, J. A., and Beckman, W. A. (2013). Solar engineering of thermal processes (Vol. 3): Wiley New York.
15. Gao, Y., Long, D., and Li, Z. (2008). Estimation of daily evapotranspiration from remotely sensed data under complex terrain over the upper Chao river basin in north China. International Journal of Remote Sensing, 29 (11), 3295-3315.
16. Genanu, M., Alamirew, T., Senay, G., and Gebremichael, M. (2017). Remote Sensing Based Estimation of Evapo-Transpiration Using Selected Algorithms: The Case of Wonji Shoa Sugar Cane Estate, Ethiopia. Journal of Environment and Earth Science, 7 (1), 46-59.
17. Irmak, S., Haman, D., and Jones, J. (2002). Evaluation of class A pan coefficients for estimating reference evapotranspiration in humid location. Journal of Irrigation and Drainage Engineering, 128(3), 153-159.
18. Jana, C., Rawat, M., Sena, D., Alam, N., Mandal, U., Kaushal, R., andd Mishra, P. (2016). Application of SEBAL model to estimate Evapotranspiration in Doon Valley, India. Indian Journal of Soil Conservation, 44(2), 191-197.
19. Li, S., and Zhao, W. (2010). Satellite based actual evapotranspiration estimation in the middle reach of the Heihe River Basin using the SEBAL method. Hydrological Processes, 24 (23), 3337-3344.
20. National Water Commission. (2009). Assessment of Evaporation Losses from the Menindee Lakes using SEBAL Remote Sensing Technology. water watch
21. Rahimi, S., Gholami Sefidkouhi, M.A., Raeini-Sarjaz, M., and Valipour, M. (2015). Estimation of actual evapotranspiration by using MODIS images (a case study: Tajan catchment). Archives of Agronomy and Soil Scie, 61(5), 695-709.
22. Sima, S., Ahmadalipour, A., and Tajrishy, M. (2013). Mapping surface temperature in a hyper-saline lake and investigating the effect of temperature distribution on the lake evaporation. Remote Sensing of Environment, 136, 374-385.
23. Sun Z., Wei B., Su W., Shen, W., Wang C., You D., and Liu, Z, (2011). Evapotranspiration estimation based on the SEBAL model in the Nansi Lake Wetland of China. Mathematical and Computer Modelling, 54 (3–4), 1086-1092.
24. Yang, J.Y., Mei, X.R., Huo, Z.G., Yan, C.-., Hui, J., Zhao, F.H., and Qin, L. (2015). Water consumption in summer maize and winter wheat cropping system based on SEBAL model in Huang-Huai-Hai Plain, China. Journal of Integrative Agriculture, 14, 2065-2076.
25. Zare, A. H., Yazdani, V., and Azhdari, K. (2009). Comparative study of four meteorological drought index based on relative yield of rain fed wheat in Hamedan province. Physical Geography Research Quarterly, (69), 35-49.