Estimation of evapotranspiration from ground-based meteorological data and global land data assimilation system (GLDAS)

  • Jongmin Park
  • Minha Choi
Original Paper


Evapotranspiration (ET) is one of the most significant factors in understanding global hydrological budgets, and its accurate estimation is crucial for understanding water balance and developing efficient water resource management plans. For calculation of reference ET (ETref), the meteorological data from weather stations have been widely used for estimation at the point scale; however, meteorological data from the global land data assimilation system (GLDAS) at the regional scale are rarely used for the estimation of ET. In this study, 30 different equations provided in the Reference Evapotranspiration Calculator Software (REF-ET) were utilized for estimating ETref with GLDAS and point scale data collected at 14 observation sites in the Korean Peninsula during 2013. Using ETref calculated from observation and GLDAS, 30 equations were evaluated by estimating the overall rank number, as determined by the correlation coefficient, normalized standard deviation, bias, and root mean square error (RMSE). Results showed that the Penman (Proc R Soc Lond Ser A Math Phys Sci 193:120–145, 1948) FAO-56 Penman–Monteith, 1982 Kpen equation (combination equations), the 1957 Makkink, Priestley–Taylor equation (radiation based equation), and the 1985 Hargreaves equation had a good overall rank. Using the six selected equations, seasonal analysis was conducted and evaluated using the bias and RMSE. Comparison of the ETref gathered from observation and GLDAS revealed that both of them showed similar seasonal variation, although ETref calculated from GLDAS were underestimated. Sensitivity analysis conducted by changing three main climatic variables (i.e., temperature, wind speed, and sunshine hours) by ±1, ±5, ±10, ±15, and ±20 % with one variable fixed also revealed that ETref was more affected by air temperature than sunshine hours and wind speed throughout the 14 selected stations.


Evapotranspiration Reference evapotranspiration GLDAS Sensitivity analysis 



This research was supported by Space Core Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2014M1A3A3A02034789). The authors would like to express their gratitude to the Korea Meteorological Administration (KMA) for providing various meteorological data for estimating ET.


  1. Adams RM, Hurd BH, Lenhart S, Leary N (1998) Effects of global climate change on agriculture: an interpretative review. Clim Res 11:19–30CrossRefGoogle Scholar
  2. Ali MH, Adham AKM, Rahman MM, Islam AKMR (2009) Sensitivity of penman-monteith estimates of reference evapotranspiration to errors in input climatic data. J Agrometeorol 11:1–8Google Scholar
  3. Allen RG (2013) REF-ET: reference evapotranspiration calculation software for FAO and ASCE standardized equations version 3.1.15 for Windows University of Idaho Research and Extension Center, Kimberly, IdahoGoogle Scholar
  4. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 FAO. Rome 300: 6541Google Scholar
  5. ASCE-EWRI (2005) The ASCE standardized reference evapotranspiration equation environmental and water resources institute of the ASCE standardization of reference evapotranspiration. Evapotranspiration Task Committee: 216Google Scholar
  6. Blaney HF, Criddle WD (1950) Determining water requirements in irrigated areas from climatological and irrigation data. U.S. Soil Conservation Service, Washington, DCGoogle Scholar
  7. Bormann H (2011) Sensitivity analysis of 18 different potential evapotranspiration models to observed climatic change at German climate stations. Clim Change 104:729–753CrossRefGoogle Scholar
  8. Brotzge JA, Crawford KC (2003) Examination of the surface energy budget: a comparison of eddy correlation and bowen ratio measurement systems. J Hydrometeorol 4:160–178CrossRefGoogle Scholar
  9. Chen Y, Shi J, Qi Y, Jiang L (2008) The simulation study on land surface energy budget over china area based on LIS-NOAH land surface model. The International archives of the photogrammetry. Remote Sens Spat Inf Sci 37:541–548Google Scholar
  10. Croitoru AE, Piticar A, Dragotǎ CS, Burada DC (2013) Recent changes in reference evapotranspiration in Romania. Glob Planet Change 111:127–132CrossRefGoogle Scholar
  11. Darshana Pandey A, Pandey RP (2013) Analysing trends in reference evapotranspiration and weather variables in the Tons River Basin in Central India. Stoch Environ Res Risk Assess 27:1407–1421CrossRefGoogle Scholar
  12. Detto M, Verfaillie J, Anderson F, Xu L, Baldocchi D (2011) Comparing laser-based open-and closed-path gas analyzers to measure methane fluxes using the eddy covariance method. Agric For Meteorol 151:1312–1324CrossRefGoogle Scholar
  13. Estévez J, Gavilán P, Berengena J (2009) Sensitivity analysis of a penman-monteith type equation to estimate reference evapotranspiration in Southern Spain. Hydrol Process 23:3342–3353CrossRefGoogle Scholar
  14. Famiglietti JS, Wood EF (1995) Effects of spatial variability and scale on areally averaged evapotranspiration. Water Resour Res 31:699–712CrossRefGoogle Scholar
  15. Ferreira VG, Andam-akorful SA, He XF, Xiao RY (2014) Estimating water storage changes and sink terms in Volta Basin from satellite missions. Water Sci Eng 7:5–16Google Scholar
  16. Forootan E et al (2014) Separation of large scale water storage patterns over Iran using GRACE, altimetry and hydrological data. Remote Sens Environ 140:580–595CrossRefGoogle Scholar
  17. Gong L, Cy Xu, Chen D, Halldin S, Chen YD (2006) Sensitivity of the Penman–Monteith reference evapotranspiration to key climatic variables in the Changjiang (Yangtze River) basin. J Hydrol 329:620–629CrossRefGoogle Scholar
  18. Gu J, Li X, Huang C (2012). Changes in satellite-derived vegetation growth trend in China from 2002 to 2010Google Scholar
  19. Hargreaves GH, Samani ZA (1982) Estimating potential evapotranspiration. J Irrig Drain Div 108:225–230Google Scholar
  20. He D et al (2013) Climate change and its effect on reference crop evapotranspiration in central and western Inner Mongolia during 1961–2009. Front Earth Sci 7:417–428CrossRefGoogle Scholar
  21. Hidalgo HG, Cayan DR, Dettinger MD (2005) Sources of variability of evapotranspiration in California. J Hydrometeorol 6:3–19CrossRefGoogle Scholar
  22. Hongwei X, Rui S, Junping D (2012). Estimation of evapotranspiration in Heihe River basin using HJ-1AB data Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International pp 202–205Google Scholar
  23. Hou L, Zou S, Xiao H, Yang Y, SpringerPlus (2013) Sensitivity of the reference evapotranspiration to key climatic variables during the growing season in the Ejina oasis northwest China. SpringOpen J 2(Suppl 1):S4Google Scholar
  24. Hwang K, Choi M (2013) Seasonal trends of satellite-based evapotranspiration algorithms over a complex ecosystem in East Asia. Remote Sens Environ 137:244–263CrossRefGoogle Scholar
  25. Irmak S, Allen R, Whitty E (2003) Daily grass and alfalfa-reference evapotranspiration estimates and alfalfa-to-grass evapotranspiration ratios in Florida. J Irrig Drain Eng 129:360–370CrossRefGoogle Scholar
  26. Irmak S, Payero JO, Martin DL, Irmak A, Howell TA (2006) Sensitivity analyses and sensitivity coefficients of standardized daily ASCE-Penman–Monteith equation. J Irrig Drain Eng 132:564–578CrossRefGoogle Scholar
  27. Irmak A, Irmak S, Martin DL (2008) Reference and crop evapotranspiration in South Central NebraskaI: comparison and analysis of grass and alfalfa-reference evapotranspiration. J Irrig Drain Eng 134:690–699CrossRefGoogle Scholar
  28. Jensen ME, Haise HR (1963) Estimating evapotranspiration from solar radiation Proceedings of the American Society of Civil Engineers. J Irrig Drain Div 89:15–41Google Scholar
  29. Jensen ME, Burman RD, Allen RG (1990) Evapotranspiration and irrigation water requirements. ASCEGoogle Scholar
  30. Kong F, Chen T, Zou L, Xu X, Chi D (2014) Analysis of reference crop evapotranspiration and complexity in Liaoning Province. J Chem Pharm Res 6:589–594Google Scholar
  31. Kousari MR, Ahani H, Hendi-zadeh R (2013) Temporal and spatial trend detection of maximum air temperature in Iran during 1960-2005. Glob Planet Change 111:97–110CrossRefGoogle Scholar
  32. Kwon H, Choi M (2011) Error assessment of climate variables for FAO-56 reference evapotranspiration. Meteorol Atmos Phys 112:81–90CrossRefGoogle Scholar
  33. Ley TW, Hill RW, Jensen DT (1994) Errors in penman-wright alfalfa reference evapotranspiration estimates. I. Model sensitivity analyses. Trans Am Soc Agric Eng 37:1853–1861CrossRefGoogle Scholar
  34. Liang L, Li L, Zhang L, Li J, Li B (2008) Sensitivity of Penman–Monteith reference crop evapotranspiration in Tao’er River Basin of northeastern China. Chin Geogr Sci 18:340–347CrossRefGoogle Scholar
  35. Liu H, Li Y, Josef T, Zhang R, Huang G (2014) Quantitative estimation of climate change effects on potential evapotranspiration in Beijing during 1951–2010. J Geogr Sci 24:93–112CrossRefGoogle Scholar
  36. Lu J, Sun G, McNulty SG, Amatya DM (2005) A comparison of six potential evapotranspiration methods for regional use in the Southeastern United States JAWRA. J Am Water Resour Assoc 41:621–633CrossRefGoogle Scholar
  37. Middelkoop H et al (2001) Impact of climate change on hydrological regimes and water resources management in the Rhine basin. Clim Change 49:105–128CrossRefGoogle Scholar
  38. Moiwo JP, Tao F, Lu W (2013) Analysis of satellite-based and in situ hydro-climatic data depicts water storage depletion in North China Region. Hydrol Process 27:1011–1020CrossRefGoogle Scholar
  39. Monteith J (1965) Evaporation and environment. Symp Soc Exp Biol 19(205–23):4Google Scholar
  40. Nandagiri L, Kovoor GM (2006) Performance evaluation of reference evapotranspiration equations across a range of Indian climates. J Irrig Drain Eng 132:238–249CrossRefGoogle Scholar
  41. Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proc R Soc Lond Ser A Math Phys Sci 193:120–145CrossRefGoogle Scholar
  42. Priestley C, Taylor R (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100:81–92CrossRefGoogle Scholar
  43. Rana G, Katerji N (1998) A measurement based sensitivity analysis of the Penman–Monteith actual evapotranspiration model for crops of different height and in contrasting water status. Theor Appl Climatol 60:141–149CrossRefGoogle Scholar
  44. Rodell M et al (2004) The global land data assimilation system. Bull Am Meteorol Soc 85:381–394CrossRefGoogle Scholar
  45. Rosenberry DO, Stannard DI, Winter TC, Martinez ML (2004) Comparison of 13 equations for determining evapotranspiration from a prairie wetland, Cottonwood Lake area, North Dakota, USA. Wetlands 24:483–497CrossRefGoogle Scholar
  46. Shenbin C, Yunfeng L, Thomas A (2006) Climatic change on the Tibetan Plateau: potential evapotranspiration trends from 1961–2000. Clim Change 76:291–319CrossRefGoogle Scholar
  47. Shuttleworth W (1993) Evaporation. In: Maidment DR (ed) Handbook of hydrology. McGraw-Hill, NewyorkGoogle Scholar
  48. Su H, Wood EF, McCabe MF, Su Z (2007) Evaluation of remotely sensed evapotranspiration over the CEOP EOP-1 reference sites. J Meteorol Soc Jpn 85A:439–459CrossRefGoogle Scholar
  49. Tabari H, Hosseinzadeh Talaee P (2014) Sensitivity of evapotranspiration to climatic change in different climates. Glob Planet Change 115:16–23CrossRefGoogle Scholar
  50. Temesgen B, Eching S, Davidoff B, Frame K (2005) Comparison of some reference evapotranspiration equations for California. J Irrig Drain Eng 131:73–84CrossRefGoogle Scholar
  51. Thornthwaite CW (1948) An approach toward a rational classification of climate. Geogr Rev 38:55–94CrossRefGoogle Scholar
  52. Turc L (1961) Estimation of irrigation water requirements, potential evapotranspiration: a simple climatic formula evolved up to date. Ann Agron 12:13–49Google Scholar
  53. Umara BG, Aliyu MM, Umaru AB, Abdullahi AS (2012) Comparison of four empirical models for estimating crop evapotranspiration in semi-arid Nigeria. Aust J Basic Appl Sci 6:26–32Google Scholar
  54. Wang K, Dickinson RE (2012) A review of global terrestrial evapotranspiration: observation, modeling, climatology, and climatic variability. Rev Geophys. doi: 10.1029/2011RG000373 Google Scholar
  55. Wang A, Zeng X (2012) Evaluation of multi reanalysis products with in situ observations over the Tibetan Plateau. J Geophys Res. doi: 10.1029/2011JD016553 Google Scholar
  56. Wright JL (1982) New evapotranspiration crop coefficients. J Irrig Drain Div 108:57–74Google Scholar
  57. Xing F, Kettner AJ, Ashton A, Giosan L, Ibáñez C, Kaplan JO (2014) Fluvial response to climate variations and anthropogenic perturbations for the Ebro River, Spain in the last 4000 years. Sci Total Environ 473:20–31CrossRefGoogle Scholar
  58. Xu CY, Chen D (2005) Comparison of seven models for estimation of evapotranspiration and groundwater recharge using lysimeter measurement data in Germany. Hydrol Process 19:3717–3734CrossRefGoogle Scholar
  59. Xu C-Y, Singh V (2002) Cross comparison of empirical equations for calculating potential evapotranspiration with data from Switzerland. Water Resour Manag 16:197–219CrossRefGoogle Scholar
  60. Xu L, Lettenmaier DP, Wood EF, Burges SJ (1994) A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J Geophys Res 99:14415–14428CrossRefGoogle Scholar
  61. Xue BL, Wang L, Li X, Yang K, Chen D, Sun L (2013) Evaluation of evapotranspiration estimates for two river basins on the Tibetan Plateau by a water balance method. J Hydrol 492:290–297CrossRefGoogle Scholar
  62. Xystrakis F, Matzarakis A (2011) Evaluation of 13 empirical reference potential evapotranspiration equations on the island of crete in southern Greece. J Irrig Drain Eng 137:211–222CrossRefGoogle Scholar
  63. Yoder RE, Odhiambo LO, Wright WC (2005) Evaluation of methods for estimating daily reference crop evapotranspiration at a site in the humid southeast United States. Appl Eng Agric 21:197–202CrossRefGoogle Scholar
  64. Zaitchik BF, Rodell M, Olivera F (2010) Evaluation of the global land data assimilation system using global river discharge data and a source-to-sink routing scheme. Water Resour Res. doi: 10.1029/2009WR007811 Google Scholar
  65. Zhang Y, Yang S, Ouyang W, Zeng H, Cai M (2010) Applying multi-source remote sensing data on estimating ecological water requirement of grassland in ungauged region. Int Soc Environ Inf Sci Annu Conf ISEIS 2:953–963Google Scholar
  66. Zhang Q, Xu CY, Chen YD, Ren L (2011) Comparison of evapotranspiration variations between the Yellow River and Pearl River basin, China. Stoch Environ Res Risk Assess 25:139–150CrossRefGoogle Scholar
  67. Zhang D, Liu X, Liu C, Bai P (2013) Responses of runoff to climatic variation and human activities in the Fenhe River, China. Stoch Environ Res Risk Assess 27:1293–1301CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Water Resources and Remote Sensing Laboratory, Department of Water Resources, Graduate School of Water ResourcesSungkyunkwan UniversitySuwonRepublic of Korea

Personalised recommendations