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

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Abstract

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.

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References

  • Adams RM, Hurd BH, Lenhart S, Leary N (1998) Effects of global climate change on agriculture: an interpretative review. Clim Res 11:19–30

    Article  Google Scholar 

  • 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 11:1–8

    Google Scholar 

  • 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, Idaho

  • 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: 6541

    Google Scholar 

  • ASCE-EWRI (2005) The ASCE standardized reference evapotranspiration equation environmental and water resources institute of the ASCE standardization of reference evapotranspiration. Evapotranspiration Task Committee: 216

  • Blaney HF, Criddle WD (1950) Determining water requirements in irrigated areas from climatological and irrigation data. U.S. Soil Conservation Service, Washington, DC

    Google Scholar 

  • Bormann H (2011) Sensitivity analysis of 18 different potential evapotranspiration models to observed climatic change at German climate stations. Clim Change 104:729–753

    Article  Google Scholar 

  • Brotzge JA, Crawford KC (2003) Examination of the surface energy budget: a comparison of eddy correlation and bowen ratio measurement systems. J Hydrometeorol 4:160–178

    Article  Google Scholar 

  • 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 37:541–548

    Google Scholar 

  • Croitoru AE, Piticar A, Dragotǎ CS, Burada DC (2013) Recent changes in reference evapotranspiration in Romania. Glob Planet Change 111:127–132

    Article  Google Scholar 

  • 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 27:1407–1421

    Article  Google Scholar 

  • 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 151:1312–1324

    Article  Google Scholar 

  • Estévez J, Gavilán P, Berengena J (2009) Sensitivity analysis of a penman-monteith type equation to estimate reference evapotranspiration in Southern Spain. Hydrol Process 23:3342–3353

    Article  Google Scholar 

  • Famiglietti JS, Wood EF (1995) Effects of spatial variability and scale on areally averaged evapotranspiration. Water Resour Res 31:699–712

    Article  Google Scholar 

  • 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 7:5–16

    Google Scholar 

  • Forootan E et al (2014) Separation of large scale water storage patterns over Iran using GRACE, altimetry and hydrological data. Remote Sens Environ 140:580–595

    Article  Google Scholar 

  • 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 329:620–629

    Article  Google Scholar 

  • Gu J, Li X, Huang C (2012). Changes in satellite-derived vegetation growth trend in China from 2002 to 2010

  • Hargreaves GH, Samani ZA (1982) Estimating potential evapotranspiration. J Irrig Drain Div 108:225–230

    Google Scholar 

  • 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 7:417–428

    Article  Google Scholar 

  • Hidalgo HG, Cayan DR, Dettinger MD (2005) Sources of variability of evapotranspiration in California. J Hydrometeorol 6:3–19

    Article  Google Scholar 

  • 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 202–205

  • 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):S4

  • Hwang K, Choi M (2013) Seasonal trends of satellite-based evapotranspiration algorithms over a complex ecosystem in East Asia. Remote Sens Environ 137:244–263

    Article  Google Scholar 

  • 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 129:360–370

    Article  Google Scholar 

  • 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 132:564–578

    Article  Google Scholar 

  • 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 134:690–699

    Article  Google Scholar 

  • Jensen ME, Haise HR (1963) Estimating evapotranspiration from solar radiation Proceedings of the American Society of Civil Engineers. J Irrig Drain Div 89:15–41

    Google Scholar 

  • Jensen ME, Burman RD, Allen RG (1990) Evapotranspiration and irrigation water requirements. ASCE

  • 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 6:589–594

    Google Scholar 

  • 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 111:97–110

    Article  Google Scholar 

  • Kwon H, Choi M (2011) Error assessment of climate variables for FAO-56 reference evapotranspiration. Meteorol Atmos Phys 112:81–90

    Article  Google Scholar 

  • Ley TW, Hill RW, Jensen DT (1994) Errors in penman-wright alfalfa reference evapotranspiration estimates. I. Model sensitivity analyses. Trans Am Soc Agric Eng 37:1853–1861

    Article  Google Scholar 

  • 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 18:340–347

    Article  Google Scholar 

  • 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 24:93–112

    Article  Google Scholar 

  • 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 41:621–633

    Article  Google Scholar 

  • Middelkoop H et al (2001) Impact of climate change on hydrological regimes and water resources management in the Rhine basin. Clim Change 49:105–128

    Article  CAS  Google Scholar 

  • 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 27:1011–1020

    Article  Google Scholar 

  • Monteith J (1965) Evaporation and environment. Symp Soc Exp Biol 19(205–23):4

    Google Scholar 

  • Nandagiri L, Kovoor GM (2006) Performance evaluation of reference evapotranspiration equations across a range of Indian climates. J Irrig Drain Eng 132:238–249

    Article  Google Scholar 

  • Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proc R Soc Lond Ser A Math Phys Sci 193:120–145

    Article  CAS  Google Scholar 

  • Priestley C, Taylor R (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100:81–92

    Article  Google Scholar 

  • 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 60:141–149

    Article  Google Scholar 

  • Rodell M et al (2004) The global land data assimilation system. Bull Am Meteorol Soc 85:381–394

    Article  Google Scholar 

  • 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 24:483–497

    Article  Google Scholar 

  • Shenbin C, Yunfeng L, Thomas A (2006) Climatic change on the Tibetan Plateau: potential evapotranspiration trends from 1961–2000. Clim Change 76:291–319

    Article  Google Scholar 

  • Shuttleworth W (1993) Evaporation. In: Maidment DR (ed) Handbook of hydrology. McGraw-Hill, Newyork

    Google Scholar 

  • 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:439–459

    Article  Google Scholar 

  • Tabari H, Hosseinzadeh Talaee P (2014) Sensitivity of evapotranspiration to climatic change in different climates. Glob Planet Change 115:16–23

    Article  Google Scholar 

  • Temesgen B, Eching S, Davidoff B, Frame K (2005) Comparison of some reference evapotranspiration equations for California. J Irrig Drain Eng 131:73–84

    Article  Google Scholar 

  • Thornthwaite CW (1948) An approach toward a rational classification of climate. Geogr Rev 38:55–94

    Article  Google Scholar 

  • Turc L (1961) Estimation of irrigation water requirements, potential evapotranspiration: a simple climatic formula evolved up to date. Ann Agron 12:13–49

    Google Scholar 

  • 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 6:26–32

    Google Scholar 

  • 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 

  • 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 

  • Wright JL (1982) New evapotranspiration crop coefficients. J Irrig Drain Div 108:57–74

    Google Scholar 

  • 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 473:20–31

    Article  Google Scholar 

  • Xu CY, Chen D (2005) Comparison of seven models for estimation of evapotranspiration and groundwater recharge using lysimeter measurement data in Germany. Hydrol Process 19:3717–3734

    Article  CAS  Google Scholar 

  • Xu C-Y, Singh V (2002) Cross comparison of empirical equations for calculating potential evapotranspiration with data from Switzerland. Water Resour Manag 16:197–219

    Article  Google Scholar 

  • 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 99:14415–14428

    Article  Google Scholar 

  • 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 492:290–297

    Article  Google Scholar 

  • 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 137:211–222

    Article  Google Scholar 

  • 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 21:197–202

    Article  Google Scholar 

  • 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 

  • 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 2:953–963

    Google Scholar 

  • 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 25:139–150

    Article  Google Scholar 

  • 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 27:1293–1301

    Article  Google Scholar 

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Acknowledgments

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.

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Correspondence to Minha Choi.

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Park, J., Choi, M. Estimation of evapotranspiration from ground-based meteorological data and global land data assimilation system (GLDAS). Stoch Environ Res Risk Assess 29, 1963–1992 (2015). https://doi.org/10.1007/s00477-014-1004-2

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