Uncertainty Analysis of Actual Evapotranspiration Estimations Using Satellite Data and Climate Databases (Case Study: Karkheh Basin)

Document Type : Research Paper


1 PhD student, Department of Irrigation and Reclamation Engineering, College of Agricultural Engineering and Technology,University of Tehran,Karaj, Iran

2 Associate Professor, Department of Irrigation and Reclamation Engineering,College of Agricultural Engineering and Technology, University of Tehran, Karaj,Iran

3 Associate Professor, Geophysics Institute,University of Tehran,،Tehran,Iran


Evapotranspiration is an important component of water balance and a key element in water resources management, especially in arid and semi-arid regions such as Iran. The purpose of this study is to investigate the uncertainty of actual evapotranspiration (ETa) estimates derived from a remote sensing-based model, i.e. Priestley–Taylor Model (PT-JPT), and two global climate datasets namely GLEAM and ERA-Interim in Karkheh basin southwest of Iran during  the 2013-2017 period. The three cornered hat (TCH) method was used to analyze the uncertainty for each spatial cell (0.25× 0.25) in the basin. The results of uncertainty analysis showed that ETERA-Interim, ETGLEAM, and ETPT- JPT data have the lowest relative uncertainty in 38%, 12.6% and 49.4% of cells, respectively. The highest percentage of cells with lowest uncertainty in Seimare, South Karkhe and Gamasiab sub-basins was correspond to ETPT-JPT model (54.4%, 72.3%, and 50%, respectively). In Gharehsoo and Kashkan sub-basins the ETERA-Interim estimations were found as the method with least uncertainty, (55.5% and 53.4%, respectively). The highest number of cells with lowest relative uncertainty belongs to ETERA-Interim. Considering the lowest uncertainty, variation of actual evapotranspiration with elevation in Karkhe basin showed that the two databases and PT-JPT model perform well at 1400 to 1800 m above sea level. ETPT-JPT model did a better job in warm dry climates. ETERA-Interim and ETGLEAM data estimations were selected as the best methods in semi-humid temperate and hyper-humid-cold climates, respectively. In cells with farm-garden and forest land use, ETGLEAM have the lowest uncertainty. Similarly, in rangelands, both ETPT-JPT and ETERA-Interim databases, and for drylands, ETERA-Interim data can be recommended. Further feasibility studies in other climates are required for more scrutiny.


Main Subjects

Akbari, M. Z., Seif, Z. and Abyane, H. (2011). Estimation of Evapotranspiration by Remote Sensing Technique under Different Climate Condition. Journal of Water and Soil, 25, 835-844. (In Farsi)
Badgley, G., Fisher, J., Jimenez, C., Tu, K. P. and Vinukollu, R. (2015).On uncertainty in global terrestrial evapotranspiration estimates from choice of input forcing datasets, J. Hydrometeorol, 16, 1449–1455.
Balsamo, G. Albergel, C. Beljaars, A. Boussetta, S. Brun, E. Cloke, H. Dee, D. Dutra, E. Munoz-Sabater, J. Pappenberger, F. de Rosnay, P. Stockdale, T. and Vitart, F.(2015). ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci. 19, 389-407.
Bastiaanssen, W. (2000). SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. Journal of hydrology, 229, 87-100.
Choudhury, J., Ahmed N.U., Idso, S.B., Reginato, R.J. and Daughtry, C.S.T. (1994). Relations between evaporation coefficients and vegetation indices studied by model simulations. Remote Sensing of Environment, 50, 1-17.
Dee, D., Uppala, S., Simmons, A., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M., Balsamo, G., Bauer, P., Bechtold, P. and Beljaars, P. (2011). The ERA-Interim reanalysis, configuration and performance of the data assimilation system, Q. J. R. Meteorol. 137, 553-597.
Feizolahpour, F. Delavar, M and Afshar, H. (2018). Evaluation and Uncertainty Analysis of Reference Crop Evapotranspiration Estimation Using Genetic Programming. Journal of Water and Soil, 27, 135-147. (In Farsi)
Fisher, J. B., Tu, K. P. and Baldocchi, D. (2008). Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites, Remote Sens. Environ, 112, 901–919.
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 ensing, 29(11), 3295-3315.
Khalili, A., Hajam, S. and Irannejad, P. (1992) Integrated water plan of Iran, 4, 1964– 1984.
Liu, Y., Qianlai, Z., Diego, M., Zhihua, P., Kicklighter, D., Zhu, Q., He, Y., Andrey, T. and Melillo, J. (2015). Evapotranspiration in Northern Eurasia: Impact of forcing uncertainties on terrestrial ecosystem model estimates. Journal of Geophysical Research: Atmospheres, 10, 23-65.
Long, D., Longuevergne, L., Bridget, A. and Scanlon, R. (2014). Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites. Water Resources Research, 14, 121-133.
Makhdom, M. R. (2014). Fundamental of Land Use Planning (14th Ed). University of Tehran Press.
McCabe, M A. Ershadi, C. Jimenez, D. G. Miralles, D. Michel, E. and Wood, E. F. (2016). The GEWEX LandFlux project: evaluation of model evaporation using tower-based and globally gridded forcing data. Geosci. Model Dev, 9, 283–305.
Miralles, D.G. Holmes, T. De Jeu, R. Gash, J. Meesters, A. and Dolman, A. (2011). Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst, 2, 453–469.
Niyogi, D. Alapaty, K. Raman, S. and Chen, F. (2009). Development and evaluation of a coupled photosynthesis-based Gas Exchange Evapotranspiration Model (GEM) for mesoscale weather forecasting applications, J. Appl. Meteorol. Climatol, 48, 349–368.
Raziei, T. and Sotoudeh, F. (2017). Investigation of the accuracy of the European Center for Medium Range Weather Forecasts (ECMWF) in forecasting observed precipitation in different climates of Iran. Journal of the Earth and Space Physics, 43.1- 10. (In Farsi)
Sahoo, A. K. Pan, M. Troy, T. Vinukollu, R. Sheffield, J. and Wood, E. (2011). Reconciling the global terrestrial water budget using satellite remote sensing, Remote Sens. Environ., 115, 1850–1865.
Sarfraz khan, M. Waqas, U. Baik, J. and Choi, M. (2018). Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach. Agricultural and Forest Meteorology 252, 256–268.
Tavella, P. and Premoli, A. (1991). Characterization of frequency standard instability by estimation of their covariance matrix, paper presented at the 23rd Annual Precise Time and Time Interval (PTTI) Applications and Planning Meeting, U.S. Naval Observatory, Pasadena. 3–5.
Tavella, P. and Premoli, A. (1994). Estimating the instabilities of N-Clocks by measuring differences of their readings, Metrologia, 30, 479– 486.
Vinukollu, R. K. Sheffield, J. Wood, E. F. Bosilovich, M. G. and Mocko, D. (2011). Multimodel Analysis of Energy and Water Fluxes: Intercomparisons between Operational Analyses, a Land Surface Model, and Remote Sensing, J. Hydrometeorol., 13, 3–26.