Evaluation of the performance of CFSR reanalysis data set for estimating reference evapotranspiration (ET0) in Turkey

Authors

DOI:

https://doi.org/10.36253/ijam-1325

Keywords:

CFSR reanalysis, Reference evapotranspiration, FAO56-PM, Turkey

Abstract

Evapotranspiration is a key process and a necessary parameter for hydrological, meteorological, and agricultural studies. However, the calculation of actual evapotranspiration is very challenging and costly. Therefore, reference evapotranspiration (ET0) calculated using meteorological data is generally preferred over actual evapotranspiration. However, it is challenging to get complete and accurate data from meteorology stations in rural and mountainous regions. This study examined the suitability of the Climate Forecast System Reanalysis (CFSR) reanalysis data set as an alternative to meteorological observation stations to compute seasonal reference evapotranspiration for seven different climatic regions of Turkey. The ET0 calculations using the CFSR reanalysis dataset for 1987-2017 were compared to data at 259 weather stations observed in Turkey. As a result of statistical evaluations, it has been determined that the most successful predicted season is winter (C’ = 0.64-0.89, SPAEF= 0.63-0.81). The most successful estimations for this season were obtained from coastal areas with low elevations. The weakest estimations were obtained for the summer season (C’ = 0.52-0.85, SPAEF= 0.59-0.77). These results show that the ET0 estimation ability of the CFSR reanalysis dataset is satisfactory for the study area. In addition, it has been observed that CFSR tends to overestimate the observation data, especially in the southern and western regions. These findings indicate that the results of the ET0 calculation using the CFSR reanalysis data set are relatively successful for the study area. However, the data should be evaluated with observation data before being used, especially in the summer models.

References

Alemayehu T., van Griensven A., Bauwens W., 2016. Evaluating CFSR and WATCH data as input to SWAT for the estimation of the potential evapotranspiration in a data-scarce Eastern-African catchment. Journal of Hydrologic Engineering, 21 (3): 1–16. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001305

Alfaro, M.D.M., Lopes I., Montenegro A.A.A., Leal B.G., 2020. CFSR- NCEP performance for weather data forecasting in the Pernambuco Semiarid, Brazil. DYNA, 87 (215): 204-213. http://doi.org/10.15446/dyna.v87n215.89952

Allen R.G., Pereira S.L., Raes D., Smith M., 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. Rome, 300 pp

Anderson M., Diak G., Gao F., Knipper K., Hain C., Eichelmann E., Hemes K., Baldocchi D., Kustas W., Yang Y.J.R.S., 2019. Impact of insolation data source on remote sensing retrievals of evapotranspiration over the California Delta. Remote Sensing, 11: 216. https://doi.org/10.3390/rs11030216

Auerbach D.A., Easton Z.M., Walter M.T., Flecker A.S., Fuka D.R., 2016. Evaluating weather observations and the Climate Forecast System Reanalysis as inputs for hydrologic modelling in the tropics, Hydrological Processes, 30 (19): 3466–3477. https://doi.org/10.1002/hyp.10860

Bandyopadhyay A., Bhadra A., Swarnakar R.K., Raghuwanshi N.S., Singh R., 2012. Estimation of reference evapotranspiration using a user-friendly decision support system: DSS_ET. Agricultural and Forest Meteorology, 154: 19–29. https://doi.org/10.1016/j.agrformet.2011.10.013

Bromwich D.H., Wang S.H., 2005. Evaluation of the NCEP-NCAR and ECMWF 15- and 40-yr reanalyses using rawinsonde data from two independent Arctic field experiments. Monthly Weather Review, 133: 3562–3578. https://doi.org/10.1175/MWR3043.1

Conceição M.A.F., 2002. Reference evapotranspiration based on Class A-pan evaporation. Scientia Agricola, 59 (3): 417-420. https://doi.org/10.1590/S0103-90162002000300001

Dee D.P., Uppala S., Simmons A., Berrisford P., Poli P., Kobayashi S., Andrae U., Balmaseda M., Balsamo G., Bauer P., 2011. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137: 553–597. https://doi.org/10.1002/qj.828

Dile Y.T., Srinivasan R., 2014. Evaluation of CFSR Climate Data for Hydrologic Prediction in Data-Scarce Watersheds: An Application in the Blue Nile River Basin. Journal of the American Water Resources Association, 50: 1226–1241. https://doi.org/10.1111/jawr.12182

Ebita A., Kobayashi S., Ota Y., Moriya M., Kumabe R., Onogi K., Harada Y., Yasui S., Miyaoka K., Takahashi K., Kamahori H., Kobayashi C., Endo H., Soma M., Oikawa Y., Ishimizu T., 2011. The Japanese 55-year Reanalysis ‘‘JRA-55’’: An interim report. SOLA, 7: 149–152. https://doi.org/10.2151/sola.2011-038

Fuka D.R., Walter M.T., MacAlister C., Degaetano A.T., Steenhuis T.S., Easton Z.M., 2013. Using the Climate Forecast System Reanalysis as weather input data for watershed models. Hydrological Processes, 28: 5613–5623. https://doi.org/10.1002/hyp.10073

Gebler S., Hendricks Franssen H.J., Putz T., Post H., Schmidt M., Vereecken H., 2015. Actual evapotranspiration and precipitation measured by lysimeters: A comparison with eddy covariance and tipping bucket. Hydrology and Earth System Sciences, 19: 2145–2161. https://doi.org/10.5194/hess-19-2145-2015

Gupta H.V., Sorooshian S., Yapo P.O., 1999. Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. Journal of Hydrologic Engineering, 4(2): 135–143, 1999. https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135)

Irvem A., Ozbuldu M., 2019. Evaluation of Satellite and Reanalysis Precipitation Products Using GIS for All Basins in Turkey. Advances of Meteorology, 2019 (4820136): 1–11. https://doi.org/10.1155/2019/4820136

Kalnay E., Kanamitsu M., Kistler R., Collins W., Deaven D., Gandin L., Iredell M., Saha S., White G., Woollen J., Zhu Y., Chelliah M., Ebisuzaki W., Higgins W., Janowiak J., Mo K.C., Ropelewski C., Wang J., Leetmaa A., Reynolds R., Jenne R., Joseph D., 1996. The NCEP NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society, 77: 437–472. https://doi.org/10.1175/1520-0477

Kanamitsu M., Ebisuzaki W., Woollen J., Yang S.K., Hnilo J.J., Fiorino M., Potter G.L., 2002. NCEP–DOE amip-ii reanalysis (r-2). Bulletin of the American Meteorological Society, 83 (11):1631–1644. https://doi.org/10.1175/BAMS-83-11-1631

Katipoglu O.M., Acar R., Senocak S., 2021. Spatio-temporal analysis of meteorological and hydrological droughts in the Euphrates Basin, Turkey. Water Supply, ws2021019. https://doi.org/10.2166/ws.2021.019

Kite G.W., Droogers P., 2000. Comparing evapotranspiration estimates from satellites, hydrological models and field data. Journal of Hydrology, 229: 3–18. https://doi.org/10.1016/S0022-1694(99)00193-6

Lang D., Zheng J., Shi J., Liao F., Ma X., Wang W., Chen X., Zhang M., 2017. A comparative study of potential evapotranspiration estimation by eight methods with FAO Penman–Monteith Method in Southwestern China. Water, 9: 1–18. https://doi.org/10.3390/w9100734

Latrech B., Ghazouani H., Lasram A., M’hamdi B.D., Mansour M., Boujelben A., 2019. Assessment of different methods for simulating actual evapotranspiration in a semi-arid environment. Italian Journal of Agro-meteorology, 2: 21-34. https://doi.org/10.13128/ijam-650

Lauri H., Räsänen T.A., Kummu M., 2014. Using reanalysis and remotely sensed temperature and precipitation data for hydrological modeling in monsoon climate: Mekong River case study. Journal of Hydrometeorology, 15: 1532–1545. https://doi.org/10.1175/JHM-D-13-084.1

Moorhead J.E., Marek G.W., Colaizzi P.D., Gowda P.H., Evett S.R., Brauer D.K., Marek T.H., Porter D.O., 2017. Evaluation of sensible heat flux and evapotranspiration estimates using a surface layer scintillometer and a large weighing lysimeter. Sensors, 17: 2316–2350. https://doi.org/10.3390/s17102350

Moriasi D.N., Arnold J.G., Van Liew M.V., Bingner R.L., Harmel R.D., Veith T.L., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3): 885–900. https://doi.org/10.13031/2013.23153

Paredes P., Martins D.S., Pereira L.S., Cadima J., Pires C., 2017. Accuracy of daily PM-ETo estimations with ERA-Interim reanalysis products. European Water, 59: 239-246.

Piñeiro G., Perelman S., Guerschman J.P., Paruelo J.M., 2008. How to evaluate models: observed vs. predicted or predicted vs. observed? Ecological Modelling, 216 (3-4): 316–322. https://doi.org/10.1016/j.ecolmodel.2008.05.006

Purnadurga G., Kumar T.L., Rao K.K., Barbosa H., Mall R.K., 2019. Evaluation of evapotranspiration estimates from observed and reanalysis data sets over Indian region. International Journal of Climatology, 39 (15): 5791–5800. https://doi.org/10.1002/joc.6189.

Rienecker M.M., Suarez M.J., Gelaro R., Todling R., Bacmeister J., Liu E., Bosilovich M.G., Shubert S.D., Takacs L., Kim G.K., Bloom S., Chen J., Collins D., Conaty A., Silva A.D., Gu W., Joiner J., Koster R.D., Luccesi R., Molod A., Owens T., Pawson S., Pegion P., Redder C.R., Reichle R., Robertson F.R., Ruddick A.G., Sienkiewicz M., Woollen J., 2011. MERRA: NASA's modern-era retrospective analysis for research and applications. Journal of Climate, 24: 3624–3648. https://doi.org/10.1175/JCLI-D-11-00015.1

Saha S., Moorthi S., Pan H.L., Wu X., Wang J., Nadiga S., Tripp P., Kistler R., Woollen J., Behringer D., Liu H., Stokes D., Grumbine R., Gayno G., Wang J., Hou Y.T., Chuang H.Y., Juang H.M.H., Sela J., Iredell M., Treadon R., Kleist D., van Delst P., Keyser D., Derber J., Ek M., Meng J., Wei H., Yang R., Lord S., van den Dool H., Kumar A., Wang W., Long C., Chelliah M., Xue Y., Huang B., Schemm J.K., Ebisuzaki W., Lin R., Xie P., Chen M., Zhou S., Higgins W., Zou C.Z., Liu Q., Chen Y., Han Y., Cucurull L., Reynolds R.W., Rutledge G., Goldberg M., 2010. The NCEP Climate Forecast System Reanalysis. Bulletin of the American Meteorological Society, 91: 1015–1058. https://doi.org/10.1175/2010BAMS3001.1

Salekin S., Burgess J., Morgenroth J., Mason E., Meason D., 2018. A comparative study of three non-geostatistical methods for optimising digital elevation model interpolation. ISPRS International Journal of Geo-Information, 7(8): 300. https://doi.org/10.3390/ijgi7080300

Santos J.F.S., Leite D.C., Severo F.A.S., Naval L.P., 2020. Validating the Mark-HadGEM2-ES and Mark-MIROC5 climate models to simulate rainfall in the last agricultural frontier of the Brazilian North and North-East Savannah. Advances in Research, 21(8): 43-54. https://doi.org/10.9734/air/2020/v21i830225

Sentelhas P.C., Gillespie T.J., Santos E.A., 2010. Evaluation of FAO Penman-Monteith and alternative methods for estimating reference evapotranspiration with missing data in Southern Ontario, Canada. Agricultural Water Management, 97: 635-644. https://doi.org/10.1016/j.agwat.2009.12.001

Seong C., Sridhar V., Billah M.M., 2018. Implications of potential evapotranspiration methods for streamflow estimations under changing climatic conditions. International Journal of Climatology, 38 (2): 896–914. https://doi.org/10.1002/joc.5218

Shi T.T., Guan D.X., Wu J.B., Wang A.Z., Jin C.J., Han S.J., 2008. Comparison of methods for estimating evapotranspiration rate of dry forest canopy: Eddy covariance, Bowen ratio energy balance, and Penman-Monteith equation. Journal of Geophysical Research, 113 (D19): D19116. https://doi.org/10.1029/2008JD010174

Srivastava P.K., Han D., Ramirez M.A.R., Islam T., 2013. Comparative assessment of evapotranspiration derived from NCEP and ECMWF global datasets through Weather Research and Forecasting model. Atmosferic Science Letters, 14: 118–125. https://doi.org/10.1002/asl2.427

Sun G., Noormets A., Chen J., McNulty S.G., 2008. Evapotranspiration estimates from eddy covariance towers and hydrologic modeling in managed forests in Northern Wisconsin, USA. Agricultural and Forest Meteorology, 148: 257-267.

Tabari H., Grismer M.E., Trajkovic S., 2013. Comparative analysis of 31 reference evapotranspiration methods under humid conditions. Irrigation Science, 31: 107–117. https://doi.org/10.1007/s00271-011-0295-z

TAGEM, 2017. Türkiye’de sulanan bitkilerin bitki su tüketimleri. https://www.tarimorman.gov.tr/TAGEM/Belgeler/yayin/Tu%CC%88rkiyede%20Sulanan%20Bitkilerin%20Bitki%20Su%20Tu%CC%88ketimleri.pdf

Tanguy M., Prudhomme C., Smith K., Hannaford J., 2018. Historical gridded reconstruction of potential evapotranspiration for the UK. Earth System Science Data, 10 (2): 951–968. https://doi.org/10.5194/essd-10-951-2018

Tian D., Martinez C.J., Graham W.D., 2014. Seasonal prediction of regional reference evapotranspiration based on Climate Forecast System Version 2. Journal of Hydrometeorology, 15: 1166–1188. https://doi.org/10.1175/JHM-D-13-087.1

Tian Y., Zhang K., Xu Y.P., Gao X., Wang J., 2018. Evaluation of potential evapotranspiration based on CMADS reanalysis dataset over China. Water, 10: 1126. https://doi.org/10.3390/w10091126

Tran T.M.A., Eitzinger J., Manschadi A.M., 2020. Response of maize yield under changing climate and production conditions in Vietnam. Italian Journal of Agrometeorology, 1: 73-84. https://doi.org/10.13128/ijam-764

Uppala S.M., Kallberg P.W., Simmons A.J., Andrae U., Bechtold V.D.C., Fiorino M., Gibson J.K., Haseler J., Hernandez A., Kelly G.A., Li X., Onogi K., Saarinen S., Sokka N., Allan R.P., Andersson E., Arpe K., Balmaseda M.A., Beljaars A.C.M., Berg L.V.D., Bidlot J., Bormann N., Caires S., Chevallier F., Dethof A., Dragosavac M., Fisher M., Fuentes M., Hagemann S., Hólm E., Hoskins B.J., Isaksen L., Janssen P.A.E.M., Jenne R., Mcnally A.P., Mahfouf J.F., Morcrette J.J., Rayner N.A., Saunders R.W., Simon P., Sterl A., Trenberth K.E., Untch A., Vasiljevic D., Viterbo P., Woollen J., 2005. The ERA-40 re-analysis. Quarterly Journal of the Royal Meteorological Society, 131: 2961–3012. https://doi.org/10.1256/qj.04.176

Willmott C.J., 1981. On the validation of models. Physical Geography, 2(2): 184–194. https://doi.org/10.1080/02723646.1981.10642213

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Published

2023-01-29

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Irvem, A., & Ozbuldu, M. (2023). Evaluation of the performance of CFSR reanalysis data set for estimating reference evapotranspiration (ET0) in Turkey. Italian Journal of Agrometeorology, (2), 49–61. https://doi.org/10.36253/ijam-1325

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No. 2 (2022)

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