Applied Research in Geographical Sciences

Applied Research in Geographical Sciences

Modeling Evaporation from Eyvashan Dam Lake: Implementing the SEBAL Algorithm and Cloud Computing on Google Earth Engine

Articles in Press
Authors
Ali Haghizadeh 1
Nooraldin Moridi 1
Leila Ghasemi 1
Atefeh Bosak 2
1 Department of Range and Watershed Management Engineering, Faculty of Natural Resources, Lorestan University, Khorramabad, Iran
2 Department of Climatology, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran
Abstract
Evaporation is considered a critical factor in water balance systems, accounting for substantial water loss from lakes. With advancements in remote sensing technologies and computational algorithms, the estimation of evaporation from water surfaces has undergone significant transformation. This study employed the SEBAL algorithm within the Google Earth Engine platform to estimate evaporation from the Ayvashan Dam reservoir. Landsat 8 satellite imagery was processed in Google Earth Engine (GEE) to compute pixel-level evapotranspiration using the SEBAL algorithm. The results revealed that across all three study dates (10/07/2024, 04/08/2024, and 26/08/2024), the evaporation rate near the center of the dam reservoir was consistently higher than in peripheral areas. Furthermore, this study demonstrates that implementing the SEBAL algorithm in the Google Earth Engine platform maintains reasonable accuracy despite challenges such as limited access to pan evaporation data and the 11 km distance between the meteorological station and study area - a finding supported by statistical metrics (RMSE = 2.4 and Nash-Sutcliffe coefficient of 0.83). Calculated evaporation rates for July, August, and September were 9.15 mm, 12.7 mm, and 9.34 mm respectively, indicating substantial water loss from the reservoir. These findings underscore the algorithm's effectiveness in evaporation estimation even under constrained ground data conditions. Given that precipitation in the study area occurs primarily as short-term episodic events with predominantly dry conditions throughout the year, water conservation during arid periods becomes particularly crucial.
Keywords
Google Earth Engine (GEE)
SEBAL Algorithm
Cloud Computing
Evaporation Modeling
Eyvashan Dam
Allen, R., Tasumi, M., Trezza, R., Bastiaanssen, W., & Waters, R. (2002). Surface Energy Balance Algorithms for L nd Adv nced r ining nd U er’ Manual-Idaho Implementation, Version 1.0, 97p
Badem, A. C., Yılmaz, R., Cesur, M. R., & Cesur, E. (2024). Advanced Predictive Modeling for Dam Occupancy Using Historical and Meteorological Data. Sustainability, 16(17), 7696. https://doi.org/10.3390/su16177696
Bowen, I.S. (1926). The Ratio of Heat Losses by Conduction and by Evaporation from any Water Surface. Phys. Rev. 27, 779–787. https://doi.org/10.1103/PhysRev.27.779
Brisco, B., Brown, R.J., Hirose, T., McNairn, H. and Staenz, K., 2014. Precision agriculture and the role of remote sensing: a review. Journal canadien de télédétection, 24, 315–327. https://doi.org/10.1080/ 07038992.1998.10855254
Evcen, A., & Y ğcı, A L (2022). Gerçek Evapotranspirasyonun Landsat Uydu Görüntüleri Kullanarak SEBAL Modeli ile He pl nm ı: Bol /Yeniç ğ Örneği Turkish Journal of Remote Sensing and GIS, 3 (2), 172-182. https://doi.org/ 10.48123/rsgis. 1126221.
Huerta, Y. C. P., Chávarri-Velarde, E., Quispe, M. M. Z., & Murillo, R. F. P. (2025). Modeling corn evapotranspiration using the SEBAL algorithm in the Peruvian highlands. Revista Ciência Agronômica, 56, e202391928. https://doi.org/10.5935/1806-6690.20250018
Kamran, K. V., Sourghali, M., & Bagheri, S. (2024). A comparative spectral assessment approach of SEBAL and SEBS for actual evaporation estimation in Ardabil Province. International Journal of Engineering and Geosciences, 9(2), 131-146. https://doi.org/10.26833/ijeg.1344679
Lei, Y., Cao, S., Hou, Y., Wu, H., Yang, P., & Feng, Q. Quantifying the Lake Evaporation from Largest Saltwater Lake of Tibetan Plateau Based on Three Energy Balance Models. Available at SSRN 5221600. https://doi.org/10.2139/ssrn.5221600
Mekonnen, Y. G., Alamirew, T., Malede, D. A., Pareeth, S., Bantider, A., & Chukalla, A. D. (2024). Tailoring the surface energy balance algorithm for land-improved (SEBALI) model using high-resolution land/use land cover for monitoring actual evapotranspiration. Agricultural Water Management, 303, 109058. https://doi.org/10.1016/j.agwat.2024.109058
Naledzani, N., Lobina, G., Abel, R. (2017). Modelling depth to groundwater level using SEBAL-based dry season potential evapotranspiration in the upper Molopo River Catchment, South Africa. Egypt. J. Remote Sens. Space Sci., 8, 3. https://doi.org/10.1016/j.ejrs.2017.08.003
Penman, H.L. (1948). Natural evaporation from open water, bare soil and grass. Proc. Royal Soc. Ser. A, 193, 120–145. https://doi.org/10.1098/rspa.1948.0037
Swinbank, W.C. (1951). The measurement of vertical transfer of heat and water vapor by eddies in the lower atmosphere. J. Meteorol, 8, 135–145
Teluguntla, P., Thenkabail, P. S., Oliphant, A., Xiong, J., Gumma, M. K., Congalton, R. G., Yadav, K., & Huete, A. (2018). A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform. ISPRS journal of photogrammetry and remote sensing, 144, 325-340.
Wang, Y., Zhang, S., & Chang, X. (2020). Evapotranspiration estimation based on remote sensing and the SEBAL model in the Bosten Lake Basin of China. Sustainability, 12(18), 7293. https://doi.org/10.3390/su12187293.
Applied Research in Geographical Sciences

Articles in Press, Accepted Manuscript
Available Online from 21 March 2007