Water Resources Management

, Volume 32, Issue 9, pp 3053–3070 | Cite as

Performance of Different Surface Incoming Solar Radiation Models and Their Impacts on Reference Evapotranspiration

  • Ali Mokhtari
  • Hamideh Noory
  • Majid Vazifedoust


Reference evapotranspiration (ET0) from FAO-Penman-Monteith equation is highly sensitive to the surface incoming solar radiation (SISR) and therefore accurate estimate of this parameter would result in more accurate estimation of ET0. In this study, the accuracy of three main approaches for SISR estimation including empirical models (Angstrom and Hargreaves-Samani), physically-based data assimilation models (Global Land Data Assimilation System-Noah, GLDAS/Noah, and National Centers of Environmental Predictions/National Center for Atmospheric Research, NCEP/NCAR), and a satellite observation model (Satellite Application Facility on Climate Monitoring, CM-SAF) were evaluated using ground-based measurements from 2012 to 2015. Then SISR outputs from introduced approaches were implemented in FAO-Penman-Monteith equation for ET0 estimation on daily and monthly basis. The Angstrom calibrated model was the most accurate model with a coefficient of determination (R2) of 0.9 and standard error of estimate (SEE) of 2.58 MJ. m−2. d−1, and GLDAS/Noah, Hargreaves-Samani, NCEP/NCAR, and CM-SAF, had lower accuracy, respectively. However, the lack of the meteorological data and required empirical coefficients are the main limitations of applying the empirical models, however, satellite-based approaches are more practical for operational purposes. The results indicated that, in spite of slight overestimation in warm months, GLDAS/Noah model had better performance with R2=0.87 and SEE = 3.5 MJ. m−2. d−1 in case of lack of meteorological data. The accuracy of ET0 derived from FAO-Penman-Monteith equation was directly depended on the accuracy of SISR estimation. The ET0 estimation error was related to SISR estimation error with a fourth-degree function and had a linear relationship with SISR error at daily and monthly scales, respectively.


Angstrom CM-SAF GLDAS Hargreaves-Samani NCEP Reference evapotranspiration 


  1. Abraha MG, Savage MJ (2008) Comparison of estimates of daily solar radiation from air temperature range for application in crop simulations. Agric For Meteorol 148(3):401–416CrossRefGoogle Scholar
  2. Aghashariatmadari Z (2011) Evaluation of different models for estimating total solar radiation at horizontal surfaces based on meteorological data, with emphasis on the performance of the angstrom model over Iran. Dissertation, University of Tehran (IN PERSIAN)Google Scholar
  3. Aladenola OO, Madramootoo CA (2014) Evaluation of solar radiation estimation methods for reference evapotranspiration estimation in Canada. Theor Appl Climatol 118(3):377–385CrossRefGoogle Scholar
  4. Allen RG (1995) Evaluation of procedures for estimating mean monthly solar radiation from air temperature. Report submitted to the United Nations Food and Agricultural Organization (FAO), Rome ItalyGoogle Scholar
  5. Allen RG (1997) Self-calibrating method for estimating solar radiation from air temperature. J Hydrol Eng 2(2):56–67CrossRefGoogle Scholar
  6. Allen RG (2000) REF-ET: reference evapotranspiration calculation software for FAO and ASCE standardized equations. University of Idaho
  7. 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(9), D05109Google Scholar
  8. Allen RG, Walter IA, Elliott RL, Howell TA, Itenfisu D, Jensen ME, Snyder RL )2005( The ASCE standardized reference evapotranspiration equation, Am Soc of Civ Eng Reston, VaGoogle Scholar
  9. Ambas VT, Baltas E (2012) Sensitivity analysis of different evapotranspiration methods using a new sensitivity coefficient. Global NEST J 14(3):335–343Google Scholar
  10. Angstrom A (1924) Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation. Q J R Meteorol Soc 50(210):121–126CrossRefGoogle Scholar
  11. Babst F, Mueller RW, Hollmann R (2008) Verification of NCEP reanalysis shortwave radiation with mesoscale remote sensing data. IEEE Geosci Remote Sens Lett 5(1):34–37CrossRefGoogle Scholar
  12. Bakhtiari B, Liaghat AM (2011) Seasonal sensitivity analysis for climatic variables of ASCE-Penman-Monteith model in the semi-arid climate. J Agric Sci Technol 13(Supplementary Issue):1135–1145Google Scholar
  13. Barkstrom BR, Smith GL (1986) The earth radiation budget experiment: science and implementation. Rev Geophys 24(2):379–390CrossRefGoogle Scholar
  14. Barkstrom B, Harrison E, Smith G, Green R, Kibler J, Cess R (1989) Earth radiation budget experiment (ERBE) archival and April 1985 results. Bull of the Am Meteorol Soc 70(10):1254–1262CrossRefGoogle Scholar
  15. Barkstrom BR, Harrison EF, Lee RB (1990) Earth radiation budget experiment. Eos, Trans Am Geophys Un 71(9):297–304CrossRefGoogle Scholar
  16. Betts AK, Chen F, Mitchell KE, Janjić ZI (1997) Assessment of the land surface and boundary layer models in two operational versions of the NCEP eta model using FIFE data. Mon Weather Rev 125(11):2896–2916CrossRefGoogle Scholar
  17. Blaney HF, Criddle, W D (1950) Determining water requirements in irrigated area from climatological irrigation data, US Department of Agriculture, soil conservation service, Tech. Pap. No. 96, 48 pp.Google Scholar
  18. Bojanowski JS (2013) Quantifying solar radiation at the earth surface with meteorological and satellite data. Dissertation, University of TwenteGoogle Scholar
  19. Bojanowski JS, Vrieling A, Skidmore AK (2013) Calibration of solar radiation models for Europe using Meteosat second generation and weather station data. Agric and For Meteorol 176:1–9CrossRefGoogle Scholar
  20. Cano D, Monget JM, Albuisson M, Guillard H, Regas N, Wald L (1986) A method for the determination of the global solar radiation from meteorological satellite data. Sol Energ 37(1):31–39CrossRefGoogle Scholar
  21. Chen F, Mitchell K, Schaake J, Xue Y, Pan HL, Koren V, Betts A (1996) Modeling of land surface evaporation by four schemes and comparison with FIFE observations. J Geophys Res: Atmos 101(D3):7251–7268CrossRefGoogle Scholar
  22. Dai Y, Zeng X, Dickinson RE, Baker I, Bonan GB, Bosilovich MG, Oleson KW (2003) The common land model. Bull Am Meteorol Soc 84(8):1013–1023CrossRefGoogle Scholar
  23. Derber JC, Parrish DF, Lord SJ (1991) The new global operational analysis system at the National Meteorological Center. Weather Forecast 6(4):538–547CrossRefGoogle Scholar
  24. Duffie JA, Beckman WA (1980) Solar engineering of thermal processes, 1st edn. Wiley, New YorkGoogle Scholar
  25. Duffie JA, Beckman WA (1991) Solar engineering of thermal processes, 2nd edn. Wiley, New YorkGoogle Scholar
  26. Duvel JP, Viollier M, Raberanto P, Kandel R (2001) The ScaRaB-Resurs earth radiation budget dataset and first results. Bull Am Meteorol Soc 82(7):1397–1408CrossRefGoogle Scholar
  27. Dybbroe A, Thoss A, Karlsson KG (2000a) The AVHRR & AMSU/MHS products of the Nowcasting SAF. In Proceedings of the 2000 Eumetsat Meteorological Satellite Data Users’ Conference, Bologna, Italy (pp. 729–736)Google Scholar
  28. Dybbroe A, Karlsson KG, Moberg M, Thoss A (2000b) A scientific report for the SAFNWC mid-term review, issue 1.1. SMHI, septemberGoogle Scholar
  29. Ek MB, Mitchell KE, Lin Y, Rogers E, Grunmann P, Koren V, Tarpley JD (2003) Implementation of Noah land surface model advances in the National Centers for environmental prediction operational mesoscale eta model. J Geophys Res: Atmos 108(D22)Google Scholar
  30. Gong L, Xu CY, 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(3):620–629CrossRefGoogle Scholar
  31. Goyal RK (2004) Sensitivity of evapotranspiration to global warming: a case study of arid zone of Rajasthan (India). Agri Water Manag 69(1):1–11CrossRefGoogle Scholar
  32. Gueymard CA, Myers DR (2008) Validation and ranking methodologies for solar radiation models. In: Modeling solar radiation at the Earth’s surface. Springer, pp. 479–509Google Scholar
  33. Guitjens JC (1982) Models of alfalfa yield and evapotranspiration. J Irrig Drain Divis 108(3):212–222Google Scholar
  34. Harbeck GE Jr (1962) A practical field technique for measuring reservoir evaporation utilizing mass-transfer theory. US Geol Surv Paper 272-E:101–105Google Scholar
  35. Hargreaves GH, Samani ZA (1982) Estimating potential evapotranspiration. J Irrig Drain Div 108(3):225–230Google Scholar
  36. Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agriculture 1(2):96–99CrossRefGoogle Scholar
  37. Harries JE, Russell JE, Hanafin JA, Brindley H (2005) The geostationary earth radiation budget project. Bull Am Meteorol Soc 86(7):945–960CrossRefGoogle Scholar
  38. Hollmann R, Mueller RW, Gratzki A (2006) CM-SAF surface radiation budget: first results with AVHRR data. Adv Sp Res 37(12):2166–2171CrossRefGoogle Scholar
  39. Inamdar AK, Guillevic PC (2015) Net surface shortwave radiation from GOES imagery—product evaluation using ground-based measurements from SURFRAD. Remote Sens 7(8):10788–10814CrossRefGoogle Scholar
  40. Jacobowitz H, Tighe RJ (1984) The earth radiation budget derived from the NIMBUS 7 ERB experiment. J Geophys Res: Atmos 89(D4):4997–5010CrossRefGoogle Scholar
  41. Jensen ME (1985) Personal communication. ASAE National Conference, Chicago, ILGoogle Scholar
  42. Jensen ME, Burman RD, Allen RG (1990) Evapotranspiration and irrigation water requirements. ASCEGoogle Scholar
  43. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Zhu Y (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77(3):437–471.425CrossRefGoogle Scholar
  44. Kandel R, Viollier M, Raberanto P, Duvel JP (1998) The ScaRaB earth radiation budget dataset. Bull Am Meteorol Soc 79(5):765–783CrossRefGoogle Scholar
  45. Kistler R, Collins W, Saha S, White G, Woollen J, Kalnay E, van den Dool H (2001) The NCEP–NCAR 50–year reanalysis: monthly means CD–ROM and documentation. Bull Am Meteorol Soc 82(2):247–267CrossRefGoogle Scholar
  46. Koren V, Schaake J, Mitchell K, Duan QY, Chen F, Baker JM (1999) A parameterization of snowpack and frozen ground intended for NCEP weather and climate models. J Geophys Res: Atm 104(D16):19569–19585CrossRefGoogle Scholar
  47. Koster RD, Suarez MJ (1992) Modeling the land surface boundary in climate models as a composite of independent vegetation stands. J Geophys Res: Atmos 97(D3):2697–2715CrossRefGoogle Scholar
  48. Laszlo I, Ciren P, Liu H, Kondragunta S, Tarpley JD, Goldberg MD (2008) Remote sensing of aerosol and radiation from geostationary satellites. Adv Sp Res 41(11):1882–1893CrossRefGoogle Scholar
  49. Liang X, 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: Atmos 99(D7):14415–14428CrossRefGoogle Scholar
  50. Liang S, Zhong B, Fang H (2006) Improved estimation of aerosol optical depth from MODIS imagery over land surfaces. Remote Sens Environ 104(4):416–425CrossRefGoogle Scholar
  51. Liu X, Li Y, Zhong X, Zhao C, Jensen JR, Zhao Y (2014) Towards increasing availability of the Ångström–Prescott radiation parameters across China: spatial trend and modeling. Energy Convers Manag 87:975–989CrossRefGoogle Scholar
  52. Ohmura A, Dutton EG, Forgan B, Frohlich C (1998) Baseline surface radiation network (BSRN/WCRP): new precision radiometry for climate research. Bull Am Meteorol Soc 79(10):2115–2136CrossRefGoogle Scholar
  53. Paulescu M, Paulescu E, Gravila P, Badescu, V (2013) Solar radiation measurements. In weather modeling and forecasting of PV systems operation (pp. 17-42). Springer LondonGoogle Scholar
  54. Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proc Royal Soc London A: Math, Phys Eng Sci 193(1032):120–145CrossRefGoogle Scholar
  55. Piri J, Kisi O (2015) Modelling solar radiation reached to the earth using ANFIS, NN-ARX, and empirical models (case studies: Zahedan and Bojnurd stations). J Atmos Sol-Terr Phys 123:39–47CrossRefGoogle Scholar
  56. Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100(2):81–92CrossRefGoogle Scholar
  57. Rodell M, Houser PR, Jambor UEA, Gottschalck J (2004) The global land data assimilation system. Bull Am Meteorol Soc 85(3):381–394CrossRefGoogle Scholar
  58. Rosenberg NJ, Blad BL, Verma, SB (1983) Microclimate: the biological environment. John Wiley & SonsGoogle Scholar
  59. Samani Z, Bawazir AS, Bleiweiss M, Skaggs R, Tran VD (2007) Estimating daily net radiation over vegetation canopy through remote sensing and climatic data. J Irrig Drain Eng 133(4):291–297CrossRefGoogle Scholar
  60. Schulz J, Albert P, Behr HD, Caprion D, Deneke H, Dewitte S, Hollmann R (2009) Operational climate monitoring from space: the EUMETSAT satellite application facility on climate monitoring (CM-SAF). Atmos Chem Phys 9(5):1687–1709CrossRefGoogle Scholar
  61. Shapiro R (1972) Simple model for the calculation of the flux of solar radiation through the atmosphere. Appl Opti 11(4):760–764CrossRefGoogle Scholar
  62. Sharifi A, Dinpashoh Y (2014) Sensitivity analysis of the penman-monteith reference crop evapotranspiration to climatic variables in Iran. Water Resour Manag 28(15):5465–5476CrossRefGoogle Scholar
  63. Wang K, Wang P, Li Z, Cribb M, Sparrow M (2007) A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature. J Geophys Res: Atmos 112(D15)Google Scholar
  64. Wang F, Wang L, Koike T, Zhou H, Yang K, Wang A, Li W (2011) Evaluation and application of a fine-resolution global data set in a semiarid mesoscale river basin with a distributed biosphere hydrological model. J Geophysl Res: Atmos 116(D21)Google Scholar
  65. Xu CY, Singh VP (2002) Cross-comparison of empirical equations for calculating potential evapotranspiration with data from Switzerland. Water Resour Manag 16(3):197–219CrossRefGoogle Scholar
  66. Zhang H, Pu Z (2010) Beating the uncertainties: ensemble forecasting and ensemble-based data assimilation in modern numerical weather prediction. Adv in Meteorol 2010Google Scholar

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Ali Mokhtari
    • 1
  • Hamideh Noory
    • 2
  • Majid Vazifedoust
    • 3
  1. 1.National Center for Satellite ObservationIranian Space AgencyAlborzIran
  2. 2.Department of Irrigation and Reclamation EngineeringUniversity of TehranTehranIran
  3. 3.Department of Water EngineeringUniversity of GuilanRashtIran

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