Projecting the Impacts of Future Climate Change on Maximum and Minimum Temperatures in the Kashafrud River Catchment in Iran

Articles in Press

Document Type : Original Article

Authors

1 PhD student in Meteorology, Hakim Sabzevari University, Sabzevar, Iran

2 Associate Professor, Department of Meteorology, Hakim Sabzevari University, Sabzevar, Iran

3 Associate Professor, Department of Geography, Faculty of Literature and Humanities, University of Birjand, Iran

Abstract

Climate change is increasingly being recognized as one of the most significant challenges facing nations worldwide. The Kashafrud River, a vital watercourse in northeastern Iran, is particularly vulnerable to climate change. This study aimed to project future changes in maximum and minimum temperatures in the Kashafrud River basin under different climate change scenarios. Daily temperature data from 11 meteorological stations were used in this study. To project future climate, CMIP6 GCMs were employed, including ACCESS-ESM1, MRI-ESM2-0, and MIROC6. The DM method was used to correct the bias in the projected temperature data based on statistical metrics. Subsequently, the CMhyd model was utilized to downscale the maximum and minimum temperature projections under both optimistic (SSP1-2.6) and pessimistic (SSP5-8.5) scenarios for two future periods: near-future (2025-2054) and mid-future (2055-2084). The model performance was evaluated using various statistical metrics, including the coefficient of determination (R², RMSE, and KGE). The analysis of seasonal changes in the maximum and minimum temperatures revealed that the pessimistic scenario projects more severe and widespread warming across all regions and periods. These findings highlight the complex nature of climate change and its varying impact on different regions. Further analysis of annual mean temperature changes using the selected model (MIROC6) indicates that the Kashafrud River Basin will experience accelerated warming in the mid-future compared to the baseline period (1991-2020). The annual maximum temperature is projected to increase by 1.11°C and 1.97°C under the optimistic scenario for the near- and mid-future, respectively, and by 1.70°C and 3.74°C under the pessimistic scenario. Similarly, the annual minimum temperature is projected to increase by 0.98°C and 1.63°C under the optimistic scenario for the near future and mid-future, respectively, and by 1.59°C and 2.48°C under the pessimistic scenario. Projected temperature rises, especially in mountainous and snow-covered areas, significantly impact ecosystems and water availability, worsening environmental degradation, and intensifying extreme events.

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References

References (in Persian)
Ahani, E., Ziaee, S., Mohammadi, H., Mardani Najafabadi, M., & Mirzaee, A. (2024). Evaluation and Simulation of the water footprint of agricultural products in climate change scenarios: A case study of Kashf Roud Basin. JOURNAL OF AGRICULTURAL SCIENCE AND SUSTAINABLE PRODUCTION, 34(1), 287-304. [In Persian].
Ataei, H., Kouhi, M., Modirian, R., & Bazrafshan, B. (2021). Projected Changes in Temperature and Precipitation over Kashafrood Basin Based on Statistical and Dynamical Downscaling Methods. Journal of Natural Environmental Hazards, 10(30), 183-202. [In Persian].
Babaeian, E., Nagafineik, Z., Zabolabasi, F., Habeibei, M., Adab, H., & Malbisei, S. (2009). Climate change assessment over Iran during 2010-2039 by using statistical downscaling of the ECHO-G model. Geography and Development, 7(16), 135-152. [In Persian].
Babaeian, I., Modirian, R., Khazanedari, L., Karimian, M., Kouzegaran, S., Kouhi, M., Falamarzi, Y., & Malbusi, S. (2023). Projection of Iran’s precipitation in the 21st Century using downscaling of selected CMIP6 Models by CMHyd. Earth and Space Physics, 49(2), 431-449. [In Persian].
Farzandi, M., Rezayie-pazhand, H., & Seyyednejad-Golkhatmi, N. (2014). Change-point detection of annual temperature by using the grey relational system for analyzing Mashhad’s heat island. Journal of Natural Environmental Hazards, 3(4), 49–60. [In Persian].
Kamal, A., & Massah Bavani, A. (2012). Comparison of future uncertainty of AOGCM-TAR and AOGCM-AR4 models in the projection of runoff basin. Journal of the Earth and Space Physics, 38(3), 175–188. [In Persian].
Kazemi Roshkhari, I., Asadi Vaighan, A., & Azari, M. (2024). Impacts of Climate Change and Land Use on the Discharge of Kashfrood Basin Using SWAT Model. JWSS-Isfahan University of Technology, 28(1), 93-109. [In Persian].
Rashidighane, M., Mottoli, S., Janbazghobadi, G., & Kohi, M. (2023). Evaluating the capability of three statistical methods of micro-scale scaling of CMIP6 models temperature and precipitation output in the Kashf Rood watershed. Climatology Research, 2023(53), 117–132. [In Persian].
Rezaei, M. (2023, September 23). The necessity of national action to promote adaptation to climate change (Expert Report No. 19320). Research Center of the Islamic Consultative Assembly. URL:https://www.sid.ir/paper/1090712/fa. [In Persian].
Zarrin, A., & Dadashi-Roudbari, A. (2021). Projected changes in temperature over Iran by 2040 based on CMIP6 multi-model ensemble. Physical Geography Research, 53(1), 75-90. [In Persian].
Zarrin, A., Dadashi-rodbari, A., & Salehabadi, N. (2021). Projected temperature anomalies and trends in different climate zones in Iran based on CMIP6. Iranian Journal of Geophysics, 15(1), 35-54. [In Persian].
Zenozi Alamdari, N., Sobhani, B., Eshahi, M., & Mohammadi, M. (2025). Precipitation and temperature zoning of Khorasan Razavi province using data from the sixth climate change report (CMIP6). Journal of Environmental Science Studies, 9(4), 9761-9753. [In Persian]
 
References (in English)
Afsari, R., Nazari-Sharabian, M., Hosseini, A., & Karakouzian, M. (2024). A CMIP6 Multi-Model Analysis of the Impact of Climate Change on Severe Meteorological Droughts through Multiple Drought Indices—Case Study of Iran’s Metropolises. Water, 16(5), 711.
Allen, M., Antwi-Agyei, P., Aragon-Durand, F., Babiker, M., Bertoldi, P., Bind, M., ... & Zickfeld, K. (2019). Technical Summary: Global warming of 1.5 C. An IPCC Special Report on the impacts of global warming of 1.5 C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. URL:https://pure.iiasa.ac.at/id/eprint/15716/.
Baker, N. C., & Huang, H. P. (2014). A comparative study of precipitation and evaporation between CMIP3 and CMIP5 climate model ensembles in semiarid regions. Journal of Climate, 27(10), 3731-3749.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937-1958.
Guo, J., Yan, Y., Chen, D., Lv, Y., Han, Y., Guo, X., ... & Zhai, P. (2020). The response of warm-season precipitation extremes in China to global warming: an observational perspective from radiosonde measurements. Climate Dynamics, 54, 3977-3989.
Kataoka, T., Tatebe, H., Koyama, H., Mochizuki, T., Ogochi, K., Naoe, H., ... & Watanabe, M. (2020). Seasonal to decadal predictions with MIROC6: Description and basic evaluation. Journal of Advances in Modeling Earth Systems, 12(12), e2019MS002035.
Kim, H. J., Cho, K., Kim, Y., Park, H., Lee, J. W., Kim, S. J., & Chae, Y. (2020). Spatial assessment of water-use vulnerability under future climate and socioeconomic scenarios within a River Basin. Journal of Water Resources Planning and Management, 146(7), 05020011.
Lee, J. Y., & Wang, B. (2014). Future change of global monsoon in the CMIP5. Climate Dynamics, 42, 101-119.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885-900.
Nikakhtar, M., Rahmati, S. H., Massah Bavani, A. R., & Babaeian, I. (2024). Mitigating the adverse impacts of climate change on river water quality through adaptation strategies: A Case Study of the Ardak Catchment, Northeast Iran. Theoretical and Applied Climatology, 155(9), 9131-9147.
Papalexiou, S. M., & Montanari, A. (2019). Global and regional increase of precipitation extremes under global warming. Water Resources Research, 55(6), 4901-4914.
Piani, C., Weedon, G. P., Best, M., Gomes, S. M., Viterbo, P., Hagemann, S., & Haerter, J. O. (2010). Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. Journal of Hydrology, 395(3-4), 199-215.
Raghavan, S. V., Liu, J., Nguyen, N. S., Vu, M. T., & Liong, S. Y. (2018). Assessment of CMIP5 historical simulations of rainfall over Southeast Asia. Theoretical and Applied Climatology, 132, 989-1002.
Rashid, I., Romshoo, S. A., Chaturvedi, R. K., Ravindranath, N. H., Sukumar, R., Jayaraman, M., ... & Sharma, J. (2015). Projected climate change impacts on vegetation distribution over Kashmir Himalayas. Climatic Change, 132, 601-613.
Rathjens, H., Bieger, K., Srinivasan, R., & Arnold, J. G. (2016). CMhyd user manual. Doc Prep Simulated Clim Change Data Hydrol. Impact Study, 1413. URL:http://swat.tamu.edu/software/cmhyd.
Rivera, J. A., & Arnould, G. (2020). Evaluation of the ability of CMIP6 models to simulate precipitation over Southwestern South America: Climatic features and long-term trends (1901–2014). Atmospheric Research, 241, 104953. Doi: 10.1016/j.atmosres.2020.104953.
Shiftehsome'e, B., Ezani, A., & Tabari, H. (2012). Spatiotemporal trends and change point of precipitation in Iran. Atmospheric research, 113, 1-12.
Stott, P. A., Christidis, N., Otto, F. E., Sun, Y., Vanderlinden, J. P., van Oldenborgh, G. J., ... & Zwiers, F. W. (2016). Attribution of extreme weather and climate‐related events. Wiley Interdisciplinary Reviews: Climate Change, 7(1), 23-41.
Teutschbein, C., & Seibert, J. (2012). Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology, 456, 12-29.
Yan, Y., Lu, R., & Li, C. (2019). Relationship between the future projections of Sahel rainfall and the simulation biases of present South Asian and Western North Pacific rainfall in summer. Journal of Climate, 32(4), 1327-1343.
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., ... & Ishii, M. (2019). The Meteorological Research Institute Earth System Model version 2.0, MRI-ESM2. 0: Description and basic evaluation of the physical component. Journal of the Meteorological Society of Japan. Ser. II, 97(5), 931-965.
Zazulie, N., Rusticucci, M., & Raga, G. B. (2018). Regional climate of the Subtropical Central Andes using high-resolution CMIP5 models. Part II: future projections for the twenty-first century. Climate dynamics, 51, 2913-2925.
Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M., ... & Srbinovsky, J. (2020). The Australian earth system model: ACCESS-ESM1. 5. Journal of Southern Hemisphere Earth Systems Science, 70(1), 193-214.
Journal of Natural Environmental Hazards

Articles in Press, Accepted Manuscript
Available Online from 26 February 2025

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  • Receive Date: 13 December 2024
  • Revise Date: 17 February 2025
  • Accept Date: 26 February 2025

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