Abstract
Reference evapotranspiration (ETo) is a key factor in the hydrologic cycle and quantifying ETo for future periods is essential for the efficient management of water resources. The objective of this research was to project the possible changes in ETo in future periods in Iran. For this purpose, observed climate data in 40 stations across Iran were collected from 1984 to 2014 and ETo was calculated using these data and the FAO56–PM method. A multi-model ensemble of 27 CMIP6 models under two scenarios, SSP1-2.6 and SSP5-8.5, for the periods 2031–2060 and 2061–2090, was used. A linear regression (LR) model was applied for downscaling and bias correction of CMIP6 temperature using the observed temperature data. The output of the LR model was entered into an artificial neural network (ANN) model with an optimized structure, trained by the observed temperature and the calculated ETo, to project the future ETo based on the downscaled and bias-corrected CMIP6 future temperature. Results showed that ETo will increase in both 2031–2060 and 2061–2090 periods under SSP1–2.6 and SSP5–8.5 scenarios, although the rate of increment was higher under SSP5-8.5 in all stations. Considering all stations, the average of ETo changes was ~ 0.9 mm/day for 2061–2090 under SSP5-8.5. It was found that the ETo rising rate was higher in arid environments, and therefore, shifting to drier conditions with higher water demands will be expectable in such climates in the future. This could cause severe water crises in these environments.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The CMIP6 data can be downloaded from: https://climate-scenarios.canada.ca/index.php?page=cmip6-scenarios.
References
Ajjur SB, Al-Ghamdi SG (2021) Evapotranspiration and water availability response to climate change in the Middle East and North Africa. Clim Change 166(3):28. https://doi.org/10.1007/s10584-021-03122-z
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56. FAO, Rome, p 174
Araghi A et al (2015) Using wavelet transforms to estimate surface temperature trends and dominant periodicities in Iran based on gridded reanalysis data. Atmos Res 155:52–72. https://doi.org/10.1016/j.atmosres.2014.11.016
Araghi A, Adamowski J, Martinez CJ (2018) Comparison of wavelet-based hybrid models for the estimation of daily reference evapotranspiration in different climates. J Water Clim Change 11(1):39–53. https://doi.org/10.2166/wcc.2018.113
Araghi A, Martinez C, Adamowski J, Olesen JE (2018) Spatiotemporal variations of aridity in Iran using high-resolution gridded data. Int J Climatol 38(6):2701–2717. https://doi.org/10.1002/joc.5454
Araghi A, Adamowski J, Martinez CJ, Olesen JE (2019) Projections of future soil temperature in northeast Iran. Geoderma 349:11–24. https://doi.org/10.1016/j.geoderma.2019.04.034
Araghi A, Martinez CJ, Olesen JE (2023a) Evaluation of MSWX gridded data for modeling of wheat performance across Iran. Eur J Agron 144:126769. https://doi.org/10.1016/j.eja.2023.126769
Araghi A, Martinez CJ, Adamowski JF (2023b) Evaluation of TerraClimate gridded data across diverse climates in Iran. Earth Sci Inform. https://doi.org/10.1007/s12145-023-00967-z
Challinor A, Martre P, Asseng S, Thornton P, Ewert F (2014) Making the most of climate impacts ensembles. Nat Clim Change 4(2):77–80. https://doi.org/10.1038/nclimate2117
Chatterjee S, Hadi AS (2012) Regression Analysis By Example. Wiley, Hoboken, NJ, p 393
Deser C, Phillips A, Bourdette V, Teng H (2012) Uncertainty in climate change projections: the role of internal variability. Clim Dyn 38(3):527–546. https://doi.org/10.1007/s00382-010-0977-x
Dinpashoh Y, Jhajharia D, Fakheri-Fard A, Singh VP, Kahya E (2011) Trends in reference crop evapotranspiration over Iran. J Hydrol 399(3–4):422–433. https://doi.org/10.1016/j.jhydrol.2011.01.021
Ghazi B, Jeihouni E (2022) Projection of temperature and precipitation under climate change in Tabriz. Iran Arab J Geosci 15(7):621. https://doi.org/10.1007/s12517-022-09848-z
Ghazi B, Jeihouni E, Kouzehgar K, Haghighi AT (2021) Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model. Environ Earth Sci 80(12):446. https://doi.org/10.1007/s12665-021-09746-9
Ghazi B, Jeihouni E, Kisi O, Pham QB, Đurin B (2022) Estimation of Tasuj aquifer response to main meteorological parameter variations under Shared Socioeconomic Pathways scenarios. Theor Appl Climatol 149(1):25–37. https://doi.org/10.1007/s00704-022-04025-4
Gholami H, Lotfirad M, Ashrafi SM, Biazar SM, Singh VP (2023) Multi-GCM ensemble model for reduction of uncertainty in runoff projections. Stoch Environ Res Risk Assess 37(3):953–964. https://doi.org/10.1007/s00477-022-02311-1
Gondim R, Silveira C, de Souza Filho F, Vasconcelos F, Cid D (2018) Climate change impacts on water demand and availability using CMIP5 models in the Jaguaribe basin, semi-arid Brazil. Environ Earth Sci 77(15):550. https://doi.org/10.1007/s12665-018-7723-9
Huo Z, Dai X, Feng S, Kang S, Huang G (2013) Effect of climate change on reference evapotranspiration and aridity index in arid region of China. J Hydrol 492:24–34. https://doi.org/10.1016/j.jhydrol.2013.04.011
Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecasting 22:678–688. https://doi.org/10.1016/j.ijforecast.2006.03.001
IPCC (2021) The Sixth Assessment Report of the Intergovernmental. Cambridge University Press, Cambridge, UK and New York, USA, p 2391. https://doi.org/10.1017/9781009157896
Khanmohammadi N, Rezaie H, Montaseri M, Behmanesh J (2017) The effect of different meteorological parameters on the temporal variations of reference evapotranspiration. Environ Earth Sci 76(15):540. https://doi.org/10.1007/s12665-017-6871-7
Liu X, Li C, Zhao T, Han L (2020) Future changes of global potential evapotranspiration simulated from CMIP5 to CMIP6 models. Atmos Ocean Sci Lett 13(6):568–575. https://doi.org/10.1080/16742834.2020.1824983
Modaresi F, Araghinejad S, Ebrahimi K (2018) A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions. Water Resour Manage 32(1):243–258. https://doi.org/10.1007/s11269-017-1807-2
Moriasi DN et al (2007) Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Trans ASABE 50(3):885–900. https://doi.org/10.13031/2013.23153
Reis MM et al (2019) Empirical and learning machine approaches to estimating reference evapotranspiration based on temperature data. Comput Electron Agric 165:104937. https://doi.org/10.1016/j.compag.2019.104937
Shi L et al (2020) Projecting potential evapotranspiration change and quantifying its uncertainty under future climate scenarios: A case study in southeastern Australia. J Hydrol 584:124756. https://doi.org/10.1016/j.jhydrol.2020.124756
Song YH, Chung E-S, Shahid S (2022) Uncertainties in evapotranspiration projections associated with estimation methods and CMIP6 GCMs for South Korea. Sci Total Environ 825:153953. https://doi.org/10.1016/j.scitotenv.2022.153953
Wilks DS (2011) Statistical Methods in the Atmospheric Science. Academic Press, USA, International Geophysics, p 704
Yassin MA, Alazba AA, Mattar MA (2016) Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate. Agric Water Manage 163:110–124. https://doi.org/10.1016/j.agwat.2015.09.009
Zanetti SS, Sousa EF, Oliveira VP, Almeida FT, Bernardo S (2007) Estimating Evapotranspiration Using Artificial Neural Network and Minimum Climatological Data. J Irrig Drain Eng 133(2):83–89. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:2(83)
Zarrin A, Dadashi-Roudbari A (2021) Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theor Appl Climatol 144(1):643–660. https://doi.org/10.1007/s00704-021-03568-2
Zeydalinejad N, Nassery HR (2023) A review on the climate-induced depletion of Iran’s aquifers. Stoch Environ Res Risk Assess 37(2):467–490. https://doi.org/10.1007/s00477-022-02278-z
Acknowledgements
The authors would like to thank three anonymous reviewers for their comments which have greatly helped to improve this paper. The authors wish to acknowledge Iran Meteorological Organization (https://www.irimo.ir) and the Canadian center for climate modeling and analysis (https://climate-scenarios.canada.ca) for providing the datasets used in this study.
Author information
Authors and Affiliations
Contributions
Study conception and design, data collection and analysis, production of the figures, and writing the first draft were performed by A. A. with the collaboration of F.M. Construction, training, testing, and optimization of the models were performed by F. M. with the collaboration of A.A. All authors collaborated on the final manuscript.
Corresponding author
Ethics declarations
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Modaresi, F., Araghi, A. Projecting future reference evapotranspiration in Iran based on CMIP6 multi-model ensemble. Theor Appl Climatol 153, 101–112 (2023). https://doi.org/10.1007/s00704-023-04465-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-023-04465-6