Estimation and evaluvation of reference evapotranspiration using ERA5 dataset

Document Type : Research Paper

Authors

1 Researcher of Irrigation and Soil Physics, Soil and Water Research Institute, Agricultural Research and Education Organization, Karaj, Iran

2 Department of Water Science Engineering, Shahrekord University, Shahrekord, Iran.

3 Associate Professor of Irrigation and Soil Physics, Soil and Water Research Institute, Agricultural Research and Education Organization, Karaj, Iran

4 Assistant Professor in Department of Irrigation and Soil Physics, Soil and Water Research Institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.

Abstract

 
 
Appropriate estimation of reference evapotranspiration (ET0) has always been challengeas the fundamental parameter to estimate crop water requirement (ETc) in places with a data limitation forresearchers of soil and water resources.In this research, (ET0) had been estimated using the ERA5 reanalysis dataset and the by Penman–Monteith FAO-56 (PM). In the next step, the result was evaluated at the location of meteorological stations in the catchment of Karoon.Climatic data to compute the (ET0) from 26 synoptic meteorological stations of the basin and ERA5 dataset were obtained. Then, daily ET0 were calculated from 1390 to 1400.At first, variables including Tmax, Tmean, Tdew, Tmin, solar radiation, and wind speed were compared with the data of the meteorological station.The results showed that, the ERA5 dataset showed provide reliable temperature parameters and solar radiation estimates (i.e., normalized root mean square error (nRMSE) of < 30%)at the majority of cases. Although this dataset showed high nRMSE in all stations for wind speed values. These comparison between ET0 from observation and ERA5 dataset shown that at the 17 station from 26 station nRMSE less than 30 percent. Overall, the use of ERA5 dataset and estimation of ET0 in places that are faced with data scarcityfor decision- making in new planning can be a useful and reliable tool.

Keywords

Main Subjects

Extended Abstract

 

Introduction

Reference Evapotranspiration (ET0) is a complex hydrological variable that is defined by various climatic factors affecting water and energy balances. These balances are critical for determining crop water requirements and irrigation scheduling. The most common method for estimating ET0 is using meteorological data and applying the FAO Penman-Monteith equation. However, meteorological stations are often located at a distance away from agriculture areas, and may not provide accurate ET0 due to the conditions at specific locations. This has led to the development of various empirical methods for estimating ET0 based on rigorous climatic data. In places where climatic data is not readily available, the use of reanalysis datasets, such as ERA5, can provide a suitable alternative for ET0 estimation.

 

Objective

The objective of this study is to evaluate the meteorological data from the ERA5 dataset and compare the estimated ET0 values obtained from the weather station to those obtained from the reanalysis dataset.

 

Materials and methods

For the purpose of this study, data related to ET0 estimation, such as minimum temperature (Tmin), maximum temperature (Tmax), average temperature (Tmean), dew point temperature (Tdewpoint), sunshine (SR), and average relative humidity (RHmean), were collected from 26 synoptic stations in the Karun basin over the period of 2010-2020. These data was then compared to the corresponding data from the ERA5 datasets. ET0 was estimated on a daily temporal resolution using the Penman-Monteith method from FAO 56, and the results were compared to the field measurements. Finally, the accuracy of the two methods for estimating ET0 was assessed using statistical indices, including nRMSE and rMBE.

 

Results and discussion

The results of the comparison between the ERA5 dataset and the synoptic stations in the Karun basin showed that Tmax had an nRMSE of less than 30% in all stations, except for Safi Abad Dezful. Tmean had an nRMSE of more than 30% in Yasuj, Dehdz, and Farsan stations. Additionally, the variables of Tmin, Tdew, and U2 had high nRMSE values. The solar radiation variable showed an error of more than 30% in four stations, Gatvand, Shush, Azna, and Ahvaz, indicating a relatively good performance of this parameter. The evaluation of ET0 showed that in 9 out of 26 stations, the error was more than 30%. In other words, the ET0 estimated from the ERA5 dataset provided acceptable results in approximately two-thirds of the stations in the Karun catchment.

 

Conclusion

After evaluating the reference evapotranspiration and the key parameters affecting its estimation from the ERA5 dataset with the data from the synoptic stations in the Karun catchment, the results showed that the ERA5 dataset produced satisfactory results. However, the highest error was found to be related to the U2 values at the weather station locations. It is therefore suggested to use meteorological U2 values instead of wind values from the reanalysis dataset. If meteorological data is not available at the catchment scale, reanalysis datasets such as ERA5 can be implemented as a suitable alternative.

 

 

References

Allen, R.G., L.S. Pereira, D. Raes And M., Smith, 1998, Crop Evapotranspiration (Guidelines For Computing Crop Water Requirements). FAO Irrigation And Drainage Paper No. 56. Food and Agricultural Organization of the United Nations, Rome, Pp.17-18.
Andarzian, B., Bannayan, M., Steduto, P., Mazraeh, H., Barati, M. E., Barati, M.A. and Rahnama, A. (2011). Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agricultural Water Management, 100, 1–8. Available from: https://doi.org/10.1016/J.AGWAT.2011.08.023.
Asadi Oskouei, E., Delsouz Khaki, B., Kouzegaran, S., Navidi, M. N., Haghighatd, M., Davatgar, N., & Lopez-Baeza, E. (2022). Mapping Climate Zones of Iran Using Hybrid Interpolation Methods. Remote Sensing, 14(11), 2632.
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
Eyre, J. R. (1964). Progress achieved on assimilation of satellite data in numerical weather prediction over the last 30 years. In ECMWF Seminar Proceedings: Recent Developments in use of satellite observations in numerical weather prediction (pp. 1-27).
Eyre, J. R., Kelly, G. A., McNally, A. P., Andersson, E., & Persson, A. (1993). Assimilation of TOVS radiance information through one‐dimensional variational analysis. Quarterly Journal of the Royal Meteorological Society, 119(514), 1427-1463.
Galehban, E., Hamzeh, S., Veysi, sh., and Alavipanah, S.K. (2022). Estimation of Daily Reference Evapotranspiration Using Remote Sensing Data (Case Study: Sistan and Baluchestan Province). Iranian Journal of Remote Sensing & GIS, 14(2), 37-50.
Gundekar, H. G., Khodke, U. M., and Sarkar, S. (2008). Evaluation of Pan Coefficient for Reference Crop Evapotranspiration for Semi-Arid Region. Irrigation Science, 26,169–175.
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(730), 1999-2049.
Heidari Motlagh, A., Nasrolahi, A., Veysi, S., & Sharifipour, M. (2022). The Influence of Land Surface Temperature (LST) in Actual Evapotranspiration Estimated. Iranian Journal of Soil and Water Research.
     Han, W., & Xue, J. (2007, November). Adaptive tuning of background error and satellite radiances observation error for operational variational assimilation. In MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications (Vol. 6790, pp. 1063-1071). SPIE.
     Harris, B. A., & Kelly, G. (2001). A satellite radiance‐bias correction scheme for data assimilation. Quarterly Journal of the Royal Meteorological Society, 127(574), 1453-1468.
Makwana, J. J., Tiwari, M. K., and Deora, B. S. (2023). Development and comparison of artificial intelligence models for estimating daily reference evapotranspiration from limited input variables. Smart Agricultural Technology, 3, 100115.
Molaei, A., and Lashkari, H. (2020). Investigation of wind speed trend changes in central Iran using ECMWF Reanalysis data. Physical Geography Research Quarterly, 52(3), 481-498.
Nouri, M., and Homaee, M. (2022). Reference crop evapotranspiration for data-sparse regions using reanalysis products. Agricultural Water Management, 262, 107319.
Paredes, P., Martins, D.S., Pereira, L.S., Cadima, J., and Pires, C. (2018). Accuracy of daily estimation of grass referenceevapotranspiration using era-interim reanalysis products with assessment of alternative bias correctionschemes. Agricultural Water Management, 210, 340–353.
Pelosi, A., and Chirico, G. B. (2021). Regional assessment of daily reference evapotranspiration: Can ground observations be replaced by blending ERA5-Land meteorological reanalysis and CM-SAF satellite-based radiation data? Agricultural Water Management, 258, 107169.
Pelosi, A., Terribile, F., D’Urso, G., and Chirico, G. B. (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.
Raziei, T., and Parehkar, A. (2021). Performance evaluation of NCEP/NCAR reanalysis blended with observation-based datasets for estimating reference evapotranspiration across Iran. Theoretical and Applied Climatology, 144(3), 885-903.
Iranian Journal of Soil and Water Research
Volume 54, Issue 2
May 2023
Pages 353-368

Files

Share

How to cite

Statistics