Monitoring and Predicting Changes in Reference Evapotranspiration in the Moghan Plain According to CMIP6 of IPCC
Document Type : Research Paper
Authors
- Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
Abstract
Estimating the optimal water requirement requires determining the relationship between climatic conditions and evapotranspiration. As a result, water resource management relies on accurate estimation of evapotranspiration. This study aimed to monitor and predict the reference evapotranspiration, ET0, in the Moghan Plain, considering the impact of climate change. ET0 was calculated by the PMF56 method using CROPWAT software. The 30-year data (1993-2022) of the Parsabad synoptic station and the output of CMIP6, including the HadGEM3-GC31-LL model and the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios were used. The results showed that the precipitation, minimum temperature and maximum temperature in the Moghan Plain from 261 mm, 9.95 °C and 21.21 °C in the base period, respectively, will reach 361 mm, 16.04 °C and 27.68 °C at the end of the century. Under the effects of climate change, ET0 will increase in the future. The severity of the climate change impact on ET0 in the Moghan Plain is greater in the warm months, and the greatest increase will be under the SSP5-8.5 scenario. The ET0 in Moghan Plain reached 1114 mm/yr in the base period, with an increase of 20% to 1334 mm/yr at the end of the century. Considering the greater share of Iran's water consumption in the agricultural sector, especially in areas such as the Moghan Plain, ET0 changes should be considered in engineering and water resource management programs.
Keywords
Subjects
Climatology
Allahverdipour, P., & Sattari, M. T. (2023). Comparing the performance of the multiple linear regression classic method and modern data mining methods in annual rainfall modeling (Case study: Ahvaz city). Water Soil. Manage. Model., 3(2), 125-142. DOI: 10.22098/mmws.2022.11337.1120 [In Persian].
Allahverdipour, P., Ghorbani, M. A., & Asadi, E. (2024). Evaluating the effects of climate change on the climatic classification in Iran. Water Soil. Manage. Model., 4(3), 95-112. DOI: 10.22098/mmws.2023.12755.1271 [In Persian].
Allahverdipour, P., & Sattari, M. T. (2024). Investigating the Maximum Wind Speed and Wind Direction of Synoptic Stations in the East of Lake Urmia. Geogr. Environ. Hazards, 13(4), 197-221. DOI: 10.22067/geoeh.2024.86654.1464 [In Persian].
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. (1998). Crop evapotranspiration- Guidelines for computing crop water requirements. FAO Irrigation and drainage paper No.56. 300(9), D05109.
Azlak, M., & Şaylan, L. (2024). Analysing the impact of climate change on evapotranspiration in a climate-sensitive region: Example of Central Anatolia (Türkiye). Soil Water. Res., 19(1), 64-76. http://dx.doi.org/10.17221/107/2023-SWR
Babolhekami, A., Gholami Sefidkouhi, M. A., and Emadi, A. (2020). The impact of climate change on reference evapotranspiration in Mazandaran province. Soil Water. Res., 51(2), 387-401. DOI: 10.22059/ijswr.2019.285571.668266 [In Persian].
Behzadi Karimi, H., Mozafari, G., Omidvar, K., & Mazidi, A. (2022). Perspective of Spatio-temporal changes of evapotranspiration in Karun catchment basin during future periods and under greenhouse gases emission scenarios. Phys. Geogra., 14(4), 87-108. [In Persian].
Dey, P., and Mishra, A. (2017). Separating the impacts of climate change and human activities on stream flow: A review of methodologies and critical assumptions. J. Hydrol., 548, 278-290. DOI: 10.1016/j.jhydrol.2017.03.014
Dinpashoh, Y. (2006). Study of reference crop evapotranspiration in I.R. of Iran. Agri. Water Manage., 84(1), 123-129. DOI: 10.1016/j.agwat.2006.02.011
Dinpashoh, Y., Jhajharia, D., Fakheri-Fard, A., Singh, V.P., & Kahya, E. (2011). Trends in reference crop evapotranspiration over Iran. J. Hydrol., 399(3–4), 422-433. DOI: 10.1016/j.jhydrol.2011.01.021
Fakhar, M. S., & Kaviani, A. (2021). Comparison of the concepts of potential and reference evapotranspiration using lysimetric data in Qazvin Province. Environ. Water Eng., 7(4), 668-682. DOI: 10.22034/jewe.2021.279059.1535 [In Persian].
Fallah-Ghalhari, G., & Shakeri, F. (2023). Assessing the consequences of climate change on potential evapotranspiration in Iran in the coming decades. Arabian J. Geosci., 16(4), 225. DOI: 10.1007/s12517-023-11230-6
Fazeli Khiavi, A., Salahi, B., & Goodarzi, M. (2020). Assessment effects of climate change on changes in potential evapotranspiration in the Moghan Plain by RCPs. Watershed. Eng. Manage., 12(4), 977-993. DOI: 10.22092/ijwmse.2019.126245.1649 [In Persian].
Frame, B., Lawrence, J., Ausseil, A. G., Reisinger, A., & Daigneault, A. (2018). Adapting global shared socio-economic pathways for national and local scenarios. Clim. Risk Manage. , 21, 39-51. DOI: 10.1016/j.crm.2018.05.001
Gurara, M. A., Jilo, N. B., & Tolche, A. D. (2021). Impact of climate change on potential evapotranspiration and crop water requirement in Upper Wabe Bridge watershed, Wabe Shebele River Basin. Eth. Afr. Earth Sci., 180, 104223. DOI: 10.1016/j.jafrearsci.2021.104223
Heydari Tasheh Kaboud, S., & Khoshkhoo, Y. (2019). Projection and prediction of the annual and seasonal future reference evapotranspiration time scales in the West of Iran under RCP emission scenarios. Appl Res. Geogr. Sci, 19(53), 157-176. DOI: 10.29252/jgs.19.53.157 [In Persian].
Latrech, B., Hermassi, T., Yacoubi, S., Slatni, A., Jarray, F., Pouget, L., & Ben Abdallah, M. A. (2024(. Comparative analysis of climate change impacts on climatic variables and reference evapotranspiration in Tunisian semi-arid region. Agric., 14(1), 160. DOI: 10.3390/agriculture14010160
Lin, P., He, Z., Du, J., Chen, L., Zhu, X., and Li, J. (2018). Impacts of climate change on reference evapotranspiration in the Qilian Mountains of China: Historical trends and projected changes. Int. J. Climatol., 38(7), 2980-2993. DOI: 10.1002/joc.5477
Mirhosseiny, S. M. R., Ghasemieh, H., & Abdollahi, K. (2021). Prediction of monthly potential evapotranspiration under RCP scenarios in future periods (Case study: Golpayegan Basin). Iran. J. Ecohydrol., 8(1), 205-220. DOI: 10.22059/ije.2021.312220.1399 [In Persian].
Mohammadi, M., & Allahverdipour, P. (2024). Uncertainty analysis of artificial neural network (ANN) and support vector machine (SVM) models in predicting monthly river flow (Case study: Ghezelozan River). Water Soil. Manage. Model., 4(2), 311-326. DOI: 10.22098/mmws.2023.12702.1267 [In Persian].
Mousavi, S. S., Karandish, F., and Tabari, H. (2016). Temporal and spatial variation of rainfall in Iran under climate change until 2100. Irrig. Water. Eng., 7(1), 152-165. [In Persian].
Nateghi, S., Rafiiei sardooi, E., Azareh, A., and Soleimani Sardoo, F. (2022). Predicting future changes in potential evapotranspiration based on RCP scenarios in Halilrood Watershed. Watershed Eng. Manage., 13(4), 769-780. DOI: 10.22092/ijwmse.2021.353294.1874 [In Persian].
Paritaghinezhad M., Kamali H., Jamshidi S., & Abdolahipour M. (2023). Evaluating the effect of climate change on evapotranspiration of Mango Plant: A Case Study of Minab Plain. J. Water Soil Sci., 27(2), 91-103. DOI: 10.47176/jwss.27.2.48011 [In Persian].
Racsko, P., Szeidl, L., & Semenov, M. (1991). A serial approach to local stochastic weather models. Ecol. Modell., 57(1-2), 27-41. DOI: 10.1016/0304-3800(91)90053-4
Ramezani Etedali, H., Partovi, Z., & Koohi, S. (2024). Estimating changes in the amount of water harvesting from air humidity and evapotranspiration due to climate change (CMIP6). Ecohydrol., 10(4), 555-573. DOI: 10.22059/ije.2024.367096.1768 [In Persian].
Riahi, K., Van Vuuren, D. P., Kriegler, E., Edmonds, J., O’neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., & Lutz, W. (2017). The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environ. Change, 42, 68-153. DOI: 10.1016/j.gloenvcha.2016.05.009
Salahi, B., Saber, M., & Mofidi, A. (2023). Evaluation perspective of the Aras Basin reference crop evapotranspiration in future climatic condition under RCPs scenarios. Watershed Eng. Manage., 15(1), 80-95. DOI: 10.22092/ijwmse.2022.357353.1950 [In Persian].
Sanikhani, H., Kisi, O., Nikpour, M. R., & Dinpashoh. (2012). Estimation of daily pan evaporation using two different adaptive Neuro-Fuzzy computing techniques. Water Resour. Manage., 26, 4347–4365. DOI: 10.1007/s11269-012-0148-4
Sattari, M. T. , Bagheri, R. , Shirirni, K., & Allahverdipour, P. (2024). Modeling daily and monthly rainfall in tabriz using ensemble learning models and decision tree regression. Clim. Change Res., 5(18), 31-48. DOI: 10.30488/ccr.2024.433394.1192
Semenov, M. A., Brooks, R. J., Barrow, E. M., & Richardson, C. W. (1998). Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim. Res., 10(2), 95-107. DOI: 10.3354/cr010095
Talebi, H., Samadianfard, S., & Valizadeh Kamran, K. (2023). Effect of land surface temperature of MODIS sensor in estimating daily reference evapotranspiration in two different climates. Environ. Water Eng., 9(3), 367-383. DOI: 10.22034/ewe.2022.366189.1815 [In Persian].
Tao, X. E., Chen, H., Xu, C. Y., Hou, Y. K., & Jie, M. X. (2015). Analysis and prediction of reference evapotranspiration with climate change in Xiangjiang River basin, China. Water Sci. Eng., 8(4), 273-281. DOI: 10.1016/j.wse.2015.11.002
Wilby, R. L., & Harris, I. (2006). A framework for assessing uncertainties in climate change impacts: low flow scenarios for the River Thames, UK. Water Resour. Res., 42(2), W02419. DOI: 10.1029/2005WR004065
Zeraati Neyshabouri, S., Pourreza Bilondi, M., Khashei-Siuki, A., & Shahidi, A. (2022). Efficiency comparison of fuzzy regression models with the penman-monteith method in estimating of monthly reference evapotranspiration of Neyshabour Plain. Environ. Water Eng., 8(1), 205-217. DOI: 10.22034/jewe.2021.283243.1555 [In Persian].
)