Water Management in Agriculture

Water Management in Agriculture

Sensitivity Analysis of Reference Evapotranspiration and Evaluation of ERA5, CFSv2 and MERRA2 meteorological Datasets

Document Type : Original Article

Authors
1 Ph. D in Irrigation and Drainage Engineering, Agricultural Research, Education and Extension Organization (AREEO), Tabriz, Iran
2 Ph. D Candidate of Irrigation and Drainage Engineering, Department of Water Sciences, Urmia University, Urmia, Iran
Abstract
The purpose of this research was to investigate the sensitivity of reference evapotranspiration to meteorological variables and to introduce the most accurate database in providing these variables in the Urmia Lake basin. For this purpose, 24 synoptic stations were selected and their meteorological data was prepared on a daily basis between 2010 and 2019. Then, the reference evapotranspiration was calculated using the FAO-Penman-Monteith equation, and the effect of changes in meteorological variables on ET0 was investigated individually in the range of ±20%. In the next step, the accuracy of ERA5, CFSv2 and MERRA2 meteorological data was evaluated and the most accurate one was introduced. The ten-year average of reference evapotranspiration of the meteorological stations was obtained 3.1 mm d-1, and the results showed that the maximum temperature is the most influential meteorological variable on reference evapotranspiration changes. After that, wind speed and minimum temperature had the greatest effect, respectively. The value of sensitivity coefficient for maximum temperature, wind speed and minimum temperature was obtained 0.4, 0.2 and 0.1 respectively. In the review of meteorological data of ERA5, CFSv2 and MERRA2, statistical indicators showed that the ERA5 dataset has the most accuracy. Based on the results, the 10-year average of reference evapotranspiration of the ERA5 was obtained 2.86 mm d-1 and according to the CRM index, this value was 8% underestimated compared to the value obtained from meteorological stations. Finally, the values of EF indices equal to 0.92 and nRMSE equal to 0.17 put the reference evapotranspiration of the ERA5 in a suitable and reliable rank.
Keywords

تیموری، م.، خورانی، ا. و بختیاری کیا، م. 1398. مقایسه داده‌های بارش ماهواره‌ای و ایستگاه‌های هواشناسی در شبیه‌سازی رواناب ماهانه رودخانه کلم با استفاده از مدل SWAT. مهندسی و مدیریت آبخیز. 11(3 ): 562-574.
خلیلی، ع.، بذرافشان، ج. و چراغعلی‌زاده، م. 1401. بررسی تطبیقی نقشه‌های اقلیمی ایران در طبقه‌بندی دمارتن گسترش داده شده و کاربست روش برای پهنه‌بندی اقلیم جهان. هواشناسی کشاورزی. 10(1): 3-16.
دین‌پژوه، ی.، جهانبخش، س. و فروغی، م. 1397. تحلیل حساسیت تبخیر-تعرق به تغییر در پارامترهای هواشناسی در شمال‌غرب و غرب ایران. نشریه حفاظت منابع آب و خاک.  8(2): 1-14.
زنگنه، م.، قهرمان، ب. و فریدحسینی، ع. 1397. مقایسه مقادیر مشاهداتی بارش و اطلاعات بارش ماهواره‌ای PERSIANN و CMORPH-روش‌های درونیابی در مقیاس ساعتی و روزانه (مطالعه موردی: حوضه آبریز شاپور). تحقیقات منابع آب ایران. 14(4 ): 1-13.
محمدی قلعه نی، م. و شرفی، س. 1401. ارزیابی دقت پایگاه‌ داده‌های CRU TS4.05 و ERA5 برای متغیرهای بارش، دما و تبخیر تعرق  پتانسیل در اقلیم‌های مختلف ایران. نشریه آبیاری و زهکشی ایران. 16(5): 879-890.
مروج الاحکامی، ب.، ابراهیمی پاک، ن.، تافته، آ. و حسینی، ن. 1401. تحلیل حساسیت تبخیر-تعرق مرجع به پارامترهای هواشناسی (مطالعه موردی: ایستگاه‌های سینوپتیک استان یزد). تحقیقات آب و خاک ایران. 53(2): 287-303.
موسوی، س.، آخوندعلی، ع. و شهبازی، ع. 1398. تحلیل پیش‌بینی گروهی بارش مدل CFSv2 با رویکرد مدیریت منابع آب (مطالعه موردی: حوضه آبریز سد دز). تحقیقات منابع آب ایران. 15(4 ): 92-106.
میری، م.، عزیزی، ق.، محمدی، ح. و پورهاشمی، م. 1396. معرفی و ارزیابی مدل جهانی همسان‌سازی داده‌های زمینی با داده‌های مشاهده‌ای در ایران. اطلاعات جغرافیایی. 26(104) : 5-17.
Allen, R.G., Tasumi, M. and Trezza, R. 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. Journal of Irrigation and Drainage Engineering. 133(4): 380–394.
Alsamamra, H., Ruiz-Arias, J.A., Pozo-Vázquez, D. and Tovar-Pescador, J. 2009. A comparative study of ordinary and residual kriging techniques for mapping global solar radiation over southern Spain. Agricultural and Forest Meteorology. 149(8): 1343–1357.
Barideh, R. and Nasimi, F. 2022. Investigating the changes in agricultural land use and actual evapotranspiration of the Urmia Lake basin based on FAO’s WaPOR database. Agricultural Water Management. 264.
Barideh, R., Veysi, S., Ebrahimipak, N. and Davatgar, N. 2022. The challenge of reference evapotranspiration between the WaPOR data set and geostatistical methods. Irrigation and Drainage. 71(5): 1268-1279.
Blatchford, M.L., Mannaerts, C.M., Njuki, S.M., Nouri, H., Zeng, Y., Pelgrum, H., Wonink, S. and Karimi, P. 2020. Evaluation of WaPOR V2 evapotranspiration products across Africa. Hydrological Processes. 34: 3200–3221.
Food and Agriculture Organization of the United Nations (FAO). 2008. Aquastat database from water report. URL www.fao.org.
Golian, S., Javadian, M. and Behrangi, A. 2019. On the use of satellite, gauge, and reanalysis precipitation products for drought studies. Environmental Research Letters. 14(7).
Gómez, J.L., Pastoriza, F.T., Álvarez, E.G. and Oller, P.E. 2020. Comparison between Geostatistical Interpolation and Numerical Weather Model Predictions for Meteorological Conditions Mapping. Infrastructures. 5(15):1-22.
 
Gong, L., Xu, C.-Y. and Chen, D. 2005. Spatial interpolation and analyses of reference evapotranspiration and its temporal trends in Changjiang (Yangtze River) Catchment. China. Geophysical Research Abstracts. 7.
Lorenz, D.J., Otkin, J.A., Svoboda, M., Hain, C.R., Anderson, M.C. and Zhong, Y. 2017. Predicting U.S. drought monitor states using precipitation, soil moisture, and evapotranspiration anomalies. Part I: development of a nondiscrete USDM index. Journal of Hydrometeorology. 18(7): 1943–1962.
Mardikis, M.G., Kalivas, D.P. and Kollias, V.J. 2005. Comparison of interpolation methods for the prediction of rference evapotranspiration - an application in Greece. Water Resources Management. 193(19): 251–278.
Michael, O. and Mbajiorgu, C. 2020. Spatial distribution of rainfall and reference evapotranspiration in Southeast Nigeria. Agricultural Engineering International. CIGR J. 22(1): 1–8.
Park, J. and Choi, M., 2015. Estimation of evapotranspiration from ground-based meteorological data and global land data assimilation system (GLDAS). Stochastic Environmental Research and Risk Assessment. 29: 1963-1992.
Pelosi, A., Terribile, F., D’Urso, G. and Chirico, GB. 2020. Comparison of ERA5-Land and UERRA MESCAN-SURFEX Reanalysis Data with Spatially Interpolated Weather Observations for the Regional Assessment of Reference Evapotranspiration. Water. 12(6):1669.
Rata, M., Douaoui, A., Larid, M. and Douaik, A. 2020. Comparison of geostatistical interpolation methods to map annual rainfall in the Chéliff watershed, Algeria. Theoretical and Applied Climatology. 1413(141): 1009–1024.
Vanderlinden, K., Giráldez, J.V. and Meirvenne, M.Van. 2008. Spatial estimation of reference evapotranspiration in Andalusia, Spain. Journal of Hydrometeorol. 9: 242–255.
Wilcox, J.D. 2019. Total solar eclipse effects on evapotranspiration captured by groundwater fluctuations in a Southern Appalachian fen. Hydrological Processes. 33: 1538–1541.
Xiao, Y., Gu, X., Yin, S., Shao, J., Cui, Y., Zhang, Q. and Niu, Y. 2016. Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China. SpringerPlus. 51(5): 1–15.
Zhang, X., Kang, S., Zhang, L. and Liu, J. 2010. Spatial variation of climatology monthly crop reference evapotranspiration and sensitivity coefficients in Shiyang River Basin of Northwest China. Agricultural Water Management. 97: 1506–1516