Iranian Journal of Irrigation & Drainage

Iranian Journal of Irrigation & Drainage

Performance evaluation of monthly ERA5 and ERA5-Land Reanalysis precipitation data in the upstream of the Zayandehroud reservoir basin

Document Type : Original Article

Authors
1 Department of Water Engineering, Urmia University
2 Department of Water Engineering, Shahrekord University
Abstract
Precipitation is one of the main components of the hydrological cycle that describes the climate of the region and the amount of renewable water in the basin. However, due to the small number of rain gauge stations and their poor distribution, as well as the short length of observation data, the study of temporal and spatial variations in precipitation has been limited. With the help of reanalysis data, this limitation can be largely overcome. Assessing the accuracy of satellite precipitation products and reanalysis is very important for understanding their uncertainty and potential applications. The aim of this study is to evaluate the performance of two reanalysis datasets, ERA5 and ERA5-Land, in estimating monthly precipitation in the upstream of the Zayandehroud reservoir. The reanalysis data were compared with corresponding precipitation data measured at five stations: Fereidounshahr, Eskandari, Qale Shahrokh, Oregan and Chelgerd over a 35-year period (1988-2022). The coefficient of determination, Nash-Sutcliffe efficiency coefficient and Root Mean Square Error (RMSE) statistics were used for this purpose. The results showed that the reanalysis data can generally describe the monthly patterns observed in upstream of the Zayandehroud reservoir well. However, in most cases, the reanalysis data underestimated the actual precipitation values, which was more pronounced at high-precipitation stations and in high-precipitation months. The results also showed that the accuracy of the ERA5-Land data was higher than that of ERA5 at all stations except the Chelgerd station. Given the acceptable accuracy of the monthly ERA5 and ERA5-Land precipitation data upstream of the Zayandehroud Dam, these data can be used as a suitable alternative for points without rain gauge stations in the study area or as input to the distributed rainfall-runoff models.
Keywords

باریده، ر.، فرج نیا، ا. و حسن پور، ر. 1403. ارزیابی دقت داده‌های بارش مجموعه داده‌های ERA5-Land و CHIRPS. پنجمین همایش ملی مدیریت آب در مزرعه (آبیاری هوشمند). موسسه تحقیقات خاک و آب، کرج.
حیدری، س.، کریمی، م. و بیرانوند، ا. 1403. ارزیابی عملکرد داده های بازتحلیل ERA5 در تخمین بارش ایران، پژوهش‌های دانش زمین. 15(2): 1-24. doi: 10.48308/esrj.2024.104225
رضیئی, ط. و ستوده، ف. 1396. بررسی دقت مرکز اروپایی پیش‌بینی‌های میان مدت جوی (ECMWF) در پیش بینی بارش مناطق گوناگون اقلیمی ایران. فیزیک زمین و فضا. 43(1): 133-147. doi: 10.22059/jesphys.2017.57958
عزیزی، ج.، رسول زاده، ع.، رحمتی، ا.، شایقی، ا. و باختر، آ. 1399. ارزیابی عملکرد داده‌های بازتحلیل شده Era-5 در تخمین بارش روزانه و ماهانه در استان اردبیل. تحقیقات آب و خاک ایران. 51 (11): 2937- 2951. doi: 10.22059/ijswr.2020.302176.668600
غلامی، س.، فرج زاده، م. و قویدل رحیمی، ی. ۱۴۰۲. بررسی تطبیقی عملکرد پایگاه‌های داده CHIRPS و ERA5-Land در آشکارسازی خشکسالی‌های ایران. جغرافیای طبیعی. 61: 1-22. doi: 20.1001.1.20085656.1402.16.61.1
Alexandridis, V., Stefanidis, S. and Dafis, S. 2023. Evaluation of ERA5 and ERA5-Land Reanalysis Precipitation Data with Rain Gauge Observations in Greece. Environmental Sciences Proceedings. 26 : 104.
Asuero, A.G., Sayago, A. and González, A.G. 2006. The correlation coefficient: An overview. Critical Reviews in Analytical Chemistry. 36: 41–59.
Deng, X., Nie, S., Deng, W. and Cao, W. 2018. Statistical evaluation of the performance of gridded monthly precipitation products from reanalysis data, satellite estimates, and merged analyses over China. Theoretical and Applied Climatology. 132, 621–637.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J. and Thépaut, J.N. 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society. 146: 1999–2049
Huang, X., Luo, G., Chen, C., Peng, J., Zhang, C., Zhou, H., Yao, B., Ma, Z. and Xi, X. 2021. How precipitation and grazing influence the ecological functions of drought-prone grasslands on the northern slopes of the Tianshan Mountains, China? Journal of Arid Land. 13: 88–97.
Izadi, N., Karakani, E.G., Saadatabadi, A.R., Shamsipour, A., Fattahi, E. and Habibi, M. 2021. Evaluation of ERA5 precipitation accuracy based on various time scales over Iran during 2000–2018. Water. 13(18): 2538. https:// doi. org/ 10. 3390/ w1318 2538
Jiao, D., Xu, N., Yang, F. and Xu, K. 2021. Evaluation of spatial-temporal variation performance of ERA5 precipitation data in China. Scientific Reports. 11: 17956. https://doi.org/10.1038/s41598-021-97432-y
Kokkalis, P., Al Jassar, H.K., Al Sarraf, H., Nair, R. and Al Hendi, H. 2024. Evaluation of ERA5 and NCEP reanalysis climate models for precipitation and soil moisture over a semi-arid area in Kuwait. Climate Dynamics. 62: 4893–4904.
Lavers, D.A., Simmons, A., Vamborg, F. and Rodwell, M.J. 2022. An evaluation of ERA5 precipitation for climate monitoring. Quarterly Journal of the Royal Meteorological Society. 148(748): 3124–3137. https://doi.org/10.1002/qj.4351
Meng, X., Guo, J., and Han, Y. 2018. Preliminarily assessment of ERA5 reanalysis data. Journal of Marine Meteorology. 38(1): 91–99.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., and Hersbach, H. 2021. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data. 13: 4349–4383.
Sharifi, E., Steinacker, R. and Saghafian, B. (2016). Assessment of GPM-IMERG and Other Precipitation Products against Gauge Data under Different Topographic and Climatic Conditions in Iran: Preliminary Results. Remote Sensing. 8(2): 135. https://doi.org/10.3390/rs8020135
Sheffield, J., Wood, E.F., Pan, M., Beck, H., Coccia, G., Serrat-Capdevila, A. and Verbist, K. 2018. Satellite remote sensing for water resources management: Potential for supporting sustainable development in data-poor regions. Water Resources Research. 54: 9724–9758.
Tang, G., Clark, M.P., Papalexiou, S.M., Ma, Z. and Hong, Y. 2020. Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasetsRemote Sensing of Environment. 240: 111697.
Tapiador, F.J., Turk, F.J., Petersen, W., Hou, A.Y., García-Ortega, E., Machado, L.A., Angelis, C.F., Salio, P., Kidd, C., Huffman, G.J. 2012. Global precipitation measurement: Methods, datasets and applications. Atmospheric Research. 104: 70–97.
Taylor, K.E. 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres. 106(D7): 7183–7192. https:// doi. org/ 10. 1029/ 2000J D9007 19
Xie, W., Yi, S., Leng, C., Xia, D., Li, M., Zhong, Z. and Ye, J. 2022. The evaluation of IMERG and ERA5-Land daily precipitation over China with considering the influence of gauge data bias. Scientific Reports. 12: 8085. https://doi.org/10.1038/s41598-022-12307-0
Yang, M., Liu, G., Chen, T., Chen, Y. and Xia, C. 2020. Evaluation of GPM IMERG precipitation products with the point rain gauge records over Sichuan, China. Atmospheric Research. 246: 105101.
Zambrano, F., Wardlow, B., Tadesse, T., Lillo-Saavedra, M. and Lagos, O. 2017. Evaluating satellite-derived long-term historical precipitation datasets for drought monitoring in Chile. Atmospheric Research. 186: 26-42.