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Nivar

Forecasting of Drought in the Southern Part of the Aras River Basin based on the CMIP6 Multi-Model Output

Document Type : Original Article

Authors
Ali Shahi 1
Bromand Salahi 2
1 Ph.D. Student of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran
2 Professor of Climatology, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran.
10.30467/nivar.2025.521168.1334
Abstract
The aim of this paper is to forecast drought at several synoptic meteorological stations in the southern part of the Aras River basin in the next three decades. Data from the AOGCM model, including MPI-ESM1-2-HR, CMCC-CM2-SR5, and BCC-CSM2-MR, were used from the CMIP6 series of models under two scenarios, SSP2-4.5 (moderate) and SSP5-8.5 (pessimistic). The observation period and and future period were considered as 1985-2014 and 2030-2059, respectively. The precipitation output was downscaled using CMHyd software. The efficiency of the models was evaluated by calculating the KGE statistical measure, and the Linear Scaling, Power transformation, and Distribution mapping Taylor diagram methods were used to select the appropriate downscaling method. In order to reduce uncertainty using the weighted averaging method (based on rank), the ensemble model was calculated. From the monthly precipitation data at the stations of the study area with a time step of 3 months (seasonal), the SPI index was calculated for the past and future time periods. Based on the values of the standard precipitation index, the drought zoning in the study area in the past and future periods was drawn in the geographic information system environment, according to the scenarios of this study, on a seasonal scale. Calculations showed that the ensemble model produced has a weaker performance than the best single model for each station, and the best model in all selected stations in the study area is the MPI model. The results showed that the maximum seasonal drought changes in the future period compared to the past will occur in the summer season at the two stations of Tabriz and Urmia and in the spring season at the Parsabad station, which in the pessimistic scenario, drought at the Urmia station will increase by 67 percent compared to the observation period. At the Tabriz station, drought will increase by 19 percent compared to the observation period in the medium scenario. At the Parsabad station, drought will increase by 89 percent compared to the base period in the medium scenario.
Keywords
Aras River Basin
CMIP6
CMHYD
Drought
Ensemble Model
Subjects
1.    Arkhi, S., Barzegar Savasari, M., & Emadeddin, S. (1401). Assessing the performance of indices derived from remote sensing technology VCI, TCI and VHI in drought assessment using MODIS imagery (Case study: central regions of Iran). Geography and Environmental Hazards, 11(3), 189–224. doi: 10.22067/geoeh.2021.72253.1102. (In Persian)
2.    Avand, M., Moradi, H., & Hezbavi, Z. (1403). Assessment of current meteorological and hydrological drought and its future projection in the Tajan watershed. Modeling and Management of Water and Soil, 4(4), 57–78. doi: 10.22098/mmws.2023.13352.1330. (In Persian)
3.    Asadi, A., & Sanaee‑Nejad, S.H. (1404). Spatial analysis of observed precipitation in Fars province and its projection using outputs of the CanESM5 climate model. Nivar, 49(128–129), 68–79. doi:10.30467/nivar.2025.476368.1307. (In Persian)
4.    Babaeian, A., Modirian, R., Khazanehdari, L., Karimian, M., Kozehgaran, S., Kohi, M., Flamerzi, Y., & Malboosi, Sh. (1402). Iran’s precipitation outlook in the 21st century using statistical downscaling of selected CMIP6 model outputs by CMHyd software. Journal of Earth and Space Physics, 49(2), 431–449. doi: 10.22059/jesphys.2023.332410.1007436. (In Persian)
5.    Babaeian, A., Modirian, R., Khazanehdari, L., Kohi, M., Kozehgaran, S., Flamerzi, Y., Karimian, M., & Malboosi, Sh. (1400). Projection of the country’s precipitation using statistical downscaling of CMIP6 model outputs, internal project of the Climate Research Institute, Mashhad. (In Persian)
6.    Hafezparast, M., Araqi‑Nejad, Sh., & Sharif Azari, S. (1394). Sustainability criteria in evaluating integrated water resources management of the Aras river basin based on the DPSIR approach. Journal of Water and Soil Conservation Research, 22(2), 61–77. https://dor.isc.ac/dor/20.1001.1.23222069.1394.22.2.4.8. (In Persian)
7.    Rashidi Ghane, M., Motovali/Motavali, S., Janbazan Ghobadi, Gh., & Kohi, M. (1403). Frequency and characteristics of droughts in Farin Dasht of Mashhad under SSP scenarios. Environmental Hazards Management, 11(2), 85–102. doi: http//doi.org/10.22059/jhsci.2024.380699.837. (In Persian)
8.    Rashidi‑Ghane, M., Motovali/Motavali, S., Janbazan Ghobadi, Gh., & Kohi, M. (1403). Future characteristics of drought in the Kashaf (Kashafrud) basin under SSP scenarios. Nivar, 48(126–127), 169–191. doi:10.30467/nivar.2024.471479.1302. (In Persian)
9.    Ramazani E’tedali, H., & Ahmadi, M. (1403). Examining the relationship between drought indices and corn yield using the random forest method (Case study: Dasht Qazvin irrigation network). Nivar, 48(126–127), 127–137. doi:10.30467/nivar.2024.467444.1299. (In Persian)
10. Zareian, M. (1401). Impacts of climate change on temperature and precipitation of Yazd province based on combined outputs of CMIP6 models. Water and Soil Sciences, 26(2), 91–105. http://dx.doi.org/10.47176/jwss.26.2.31501. (In Persian)
11. Zarrin, A., & Dadashi Roudbari, A. (1400). Projection and homogeneity of drought indices in Iran based on multi‑model CMIP5 outputs. Climate Change Research, 7(2), 71–82. doi: 10.30488/ccr.2021.317280.1058. (In Persian)
12. Zarrin, A., Dadashi Roudbari, A., & Kadkhoda, A. (1401). Projection of drought under SSP scenarios until the end of the 21st century (Case study: Urmia Lake basin). Iranian Journal of Water and Soil Research, 53(7), 1499–1516. doi: 10.22059/ijswr.2022.343700.669278. (In Persian)
13. Salahi, B., & Vatanparast Qalehjoq, F. (1403). Monitoring agricultural drought in the Aras river basin using satellite and meteorological indices. Environmental Hazards Management, 11(3), 193–212. doi: 10.22059/jhsci.2024.384523.847. (In Persian)
14. Abdolalizadeh, F., Mohammad Khorshid Doost, A., & Jahanbakhsh Asl, S. (1402). Projection and trend assessment of temperature, precipitation, and drought in the Urmia Lake basin. Hydrogeomorphology, 10(36), 39–57. doi: 10.22034/hyd.2023.56103.1687. (In Persian)
15. Kiani Sefidan Jadid, T. (1384). Synoptic analysis of convective (showery) rainfall in the southern Aras basin. Master’s thesis, University of Tabriz, Faculty of Humanities and Social Sciences, Department of Geography.15. Kiani Sefidan Jadid, T. (1384). Synoptic analysis of convective (torrential) rainfall in the southern Aras basin. Master's thesis, University of Tabriz, Faculty of Humanities and Social Sciences, Department of Geography. (In Persian)
16. Ministry of Energy (1390). Delineation and coding of watersheds and study areas at the national level (revision in 1389–1390), Iran Water Resources Management Company, Deputy for Coordination of Watersheds, Office of Basic Water Resources Studies, Esfand 1390. (In Persian)
17. Bowell, A., Salakpi, E. E., Guigma, K., Muthoka, J. M., Mwangi, J., and Rowhani, P., 2021. Validating commonly used drought indicators in Kenya. Environmental Research Letters, 16(8), 084066. DOI 10.1088/1748-9326/ac16a2
18. Chen, W., Jiang, Z. and Li, L., 2011. Probabilistic projections of climate change over China under the SRES A1B scenario using 28 AOGCMs. Journal of Climate, 24(17), pp.4741-4756. https://doi.org/10.1175/2011JCLI4102.1
19. Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J. and Taylor, K. E., 2016. Overview of the coupled model Intercomparison project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9, pp.1937-1958. https://doi.org/10.5194/gmd-9-1937-2016, 2016.
20. Fallah Ghalhari, G. A., Yousefi, H., Hosseinzadeh, A., Alimardani, M. and Reyhani, E., 2019. Assessment of climate change in Bojnourd station in 2016-2050 using downscaling models LARS WG and SDSM. Iranian Journal of Ecohydrology, 6(1), pp.99-109. https://doi.org/10.22059/ije.2018.265918.952
21. Ge, F., Zhu, S., Luo, H., Zhi, X. and Wang, H., 2021. Future changes in precipitation extremes over Southeast Asia: insights from CMIP6 multi-model ensemble. Environmental Research Letters, 16(2), 024013.‏ http://dx.doi.org/10.1088/1748-9326/abd7ad
22. Gupta, H. V., Kling, H., Yilmaz, K. K. and Martinez, G. F., 2009. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1-2), pp.80-91.‏ https://doi.org/10.1016/j.jhydrol.2009.08.003
23. https://doi.org/10.5194/esd-12-253-2021
24. Knoben, W. J., Freer, J. E. and Woods, R. A., 2019. Inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores. Hydrology and Earth System Sciences, 23(10), pp.4323-4331. https://doi.org/10.5194/hess-23-4323-2019
25. Morsy, M., Moursy, F. I., Sayad, T. and Shaban, S., 2022. Climatological study of SPEI drought index using observed and CRU gridded dataset over Ethiopia. Pure and Applied Geophysics, 179(8), pp.3055-3073.  https://doi.org/10.1007/s00024-022-03091-z
26. Pyarali, K., Peng, J., Disse, M. and Tuo, Y., 2022. Development and application of high resolution SPEI drought dataset for Central Asia. Scientific Data, 9(172), https://doi.org/10.1038/s41597-022-01279-5
27. Rathjens, H., Bieger, K., Srinivasan, R., Chaubey, I. and Arnold, J. G., 2016. CMhyd User Manual: Documentation for preparing simulated climate change data for hydrologic impact studies. Texas: SWAT
28. Samantaray, A. K., Ramadas, M. and Panda, R. K., 2022. Changes in drought characteristics based on rainfall pattern drought index and the CMIP6 multi-model ensemble. Agricultural Water Management, 266, 107568, https://doi.org/10.1016/j.agwat.2022.107568
29. Sung, H. M., Kim, J., Shim, S. et al., 2021. Climate change projection in the twenty-first century simulated by NIMS-KMA CMIP6 model based on new GHGs concentration pathways. Asia-Pacific Journal of Atmospheric Sciences, 57, pp.851-862. https://doi.org/10.1007/s13143-021-00225-6
30. Taylor, K. E., 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), pp.7183-7192.‏ https://doi.org/10.1029/2000JD900719
31. Tegegne, G., Melesse, A. M. and Worqlul, A. W., 2020. Development of multi-model ensemble approach for enhanced assessment of impacts of climate change on climate extremes. Science of the Total Environment, 704, 135357. https://doi.org/10.1016/j.scitotenv.2019.135357
32. Uwimbabazi, J., Jing, Y., Iyakaremye, V., Ullah, I. and Ayugi, B., 2022. Observed changes in meteorological drought events during 1977–2020 over Rwanda, East Africa. Sustainability, 14(3), 1519. https://doi.org/10.3390/su14031519
33. Yoo, J. H. and Kang, I. S., 2005. Theoretical examination of a multi-model composite for seasonal prediction. Geophysical Research Letters, 32(18).‏ https://doi.org/10.1029/2005GL023513
34. Zhai, J., Mondal, S. K., Fischer, T., Wang, Y., Su, B., Huang, J., ... and Uddin, M. J., 2020. Future drought through multiple characteristics over South Asia from CMIP6 ensemble model. Atmospheric Research, 246, 105111. https://doi.org/10.1016/j.atmosres.2020.105111
Nivar
Volume 49, 130-131 - Serial Number 130
October 2025
Pages 237-254

  • Receive Date 04 May 2025
  • Revise Date 15 June 2025
  • Accept Date 29 July 2025
  • Publish Date 23 September 2025