The Impact of Climate Change on Temperature and Precipitation in Afghanistan with Emphasis on the Helmand and Hariroud Basins

Document Type : Applied Article

Authors

1 MSc, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran.

2 Associate Professor, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran.

3 Postdoctoral Researcher of Climatology, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran.

Abstract

One of the most challenging issues in water resource management is the impact of climate change on the water supply. It is necessary to examine the effects of climate change on Afghanistan, which is vital to Iran's water resources. To investigate the temperature and precipitation and their variability in Afghanistan and the two basins of Helmand and Hariroud, MSWX data was used from 1981 to 2020. To project the future climate, the output of five CMIP6 models was used for the near future (2026-2050). To reduce the uncertainty of individual models, a multi-model ensemble was generated. The area-averaged precipitation trend showed that the precipitation in Afghanistan, Helmand, and Hariroud basins has decreased by 11.2, 12.2, and 12.3 mm/decade, respectively. The area-averaged temperature has increased by 0.43, 0.45, and 0.57 oC/decade over Afghanistan, Helmand, and Hariroud basins, respectively. The results showed that the temperature in all three investigated regions will have a positive anomaly in the near future under SP2-4.5 and SSP5-8.5. On the other hand, the precipitation anomaly will be negative under SSP2-4.5 in the northern regions of Afghanistan and the two studied basins. The entire area of Afghanistan and two basins will experience a negative anomaly of precipitation under SSP5-8.5.

Keywords

Main Subjects


زرین، آذر، و داداشی رودباری، عباسعلی. (1401). بررسی مدل‌های CMIP6 در برآورد دمای ایران با تأکید بر حساسیت اقلیم ترازمند (ECS) و پاسخ اقلیم گذرا (TCR). مجله ژئوفیزیک ایران، 17(1)، 39-56. https://doi.org/10.30499/ijg.2022.344862.1430
Abbass, K., Qasim, M. Z., Song, H., Murshed, M., Mahmood, H., & Younis, I. (2022). A review of the global climate change impacts, adaptation, and sustainable mitigation measures. Environmental Science and Pollution Research, 29(28), 42539-42559. https://doi.org/10.1007/s11356-022-19718-6
Aliyar, Q., Dhungana, S., & Shrestha, S. (2022). Spatio-temporal trend mapping of precipitation and its extremes across Afghanistan (1951–2010). Theoretical and Applied Climatology, 147, 605-626. https://doi.org/10.1007/s00704-021-03851-2
Asadi-RahimBeygi, N., Zarrin, A., Mofidi, A., & Dadashi-Roudbari, A. (2024). Near-term temperature extremes in Iran using the decadal climate prediction project (DCPP). Stochastic Environmental Research and Risk Assessment, 38, 447–466. https://doi.org/10.1007/s00477-023-02579-x
Babar, Z. A., Zhi, X., Ge, F., Riaz, M., Mahmood, A., Sultan, S.,  Shad, M.A., Aslam, C. M., & Ahmad, M. F. (2016). Assessment of Southwest Asia surface temperature changes: CMIP5 20th and 21st century simulations. Pakistan Journal of Meteorology, 13(25): 1-15. https://www.prdb.pk/article/assessment-of-southwest-asia-surface-temperature-changes-cm-190
Bai, H., Xiao, D., Wang, B., Liu, D. L., Feng, P., & Tang, J. (2021). Multi‐model ensemble of CMIP6 projections for future extreme climate stress on wheat in the North China Plain. International Journal of Climatology, 41, E171-E186. https://doi.org/10.1002/joc.6674
Beck, H. E., Van Dijk, A. I., Larraondo, P. R., McVicar, T. R., Pan, M., Dutra, E., & Miralles, D. G. (2022). MSWX: Global 3-hourly 0.1 bias-corrected meteorological data including near-real-time updates and forecast ensembles. Bulletin of the American Meteorological Society, 103(3), E710-E732. https://doi.org/10.1175/BAMS-D-21-0145.1
Bevacqua, E., Zappa, G., Lehner, F., & Zscheischler, J. (2022). Precipitation trends determine future occurrences of compound hot–dry events. Nature Climate Change, 12(4), 350-355. https://doi.org/10.1038/s41558-022-01309-5
Dai, A., & Bloecker, C. E. (2019). Impacts of internal variability on temperature and precipitation trends in large ensemble simulations by two climate models. Climate dynamics, 52(1-2), 289-306. https://doi.org/10.1007/s00382-018-4132-4
Daufresne, M., Lengfellner, K., & Sommer, U. (2009). Global warming benefits the small in aquatic ecosystems. Proceedings of the National Academy of Sciences, 106(31), 12788-12793. https://doi.org/10.1073/pnas.0902080106
Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., ... & Williamson, M. S. (2019). Taking climate model evaluation to the next level. Nature Climate Change, 9(2), 102-110. https://doi.org/10.1038/s41558-018-0355-y
Farhat, F., Kashifi, M. T., Jamal, A., & Saba, I. (2022). Spatiotemporal projections of precipitation and temperature over Afghanistan based on CMIP6 global climate models. Modeling Earth Systems and Environment, 8(3), 4229-4242. https://doi.org/10.1007/s40808-022-01361-2
Fatima, E., Hassan, M., Hasson, S. U., Ahmad, B., & Ali, S. S. F. (2020). Future water availability from the western Karakoram under representative concentration pathways as simulated by CORDEX South Asia. Theoretical and Applied Climatology, 141, 1093-1108. https://doi.org/10.1007/s00704-020-03261-w
Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of hydrology, 204(1-4), 182-196. https://doi.org/10.1016/S0022-1694(97)00125-X
Kang, Y., Khan, S., & Ma, X. (2009). Climate change impacts on crop yield, crop water productivity and food security–A review. Progress in natural Science, 19(12), 1665-1674. https://doi.org/10.1016/j.pnsc.2009.08.001
Kendall, M. G. (1948). Rank correlation methods, Griffin, No. 98. Harvard Book List (edited) 1955. https://psycnet.apa.org/record/1948-15040-000
Kumar, S., Chanda, K., & Pasupuleti, S. (2020). Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India. Theoretical and Applied Climatology, 140, 343-357. https://doi.org/10.1007/s00704-020-03088-5
Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). Geoscientific Model Development, 12(7), 3055-3070. https://doi.org/10.5194/gmd-12-3055-2019
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the econometric society, 245-259. https://doi.org/10.2307/1907187
Men, B., Wu, Z., Liu, H., Tian, W., & Zhao, Y. (2020). Spatio-temporal analysis of precipitation and temperature: A case study over the Beijing–Tianjin–Hebei Region, China. Pure and Applied Geophysics, 177, 3527-3541. https://doi.org/10.1007/s00024-019-02400-3
Mishra, A. K., Özger, M., & Singh, V. P. (2011). Association between uncertainties in meteorological variables and water-resources planning for the state of Texas. Journal of Hydrologic Engineering, 16(12), 984-999. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000150
Politi, N., Vlachogiannis, D., Sfetsos, A., & Nastos, P. T. (2023). High resolution projections for extreme temperatures and precipitation over Greece. Climate Dynamics, 61(1-2), 633-667. https://doi.org/10.1007/s00382-022-06590-w
Rangwala, I., Miller, J. R., Russell, G. L., & Xu, M. (2010). Using a global climate model to evaluate the influences of water vapor, snow cover and atmospheric aerosol on warming in the Tibetan Plateau during the twenty-first century. Climate Dynamics, 34, 859-872. https://doi.org/10.1007/s00382-009-0564-1
Rehman, N., Adnan, M., & Ali, S. (2018). Assessment of CMIP5 climate models over South Asia and climate change projections over Pakistan under representative concentration pathways. International Journal of Global Warming, 16(4), 381-415. https://doi.org/10.1504/IJGW.2018.095994
Sachindra, D. A., Huang, F., Barton, A., & Perera, B. J. C. (2014). Statistical downscaling of general circulation model outputs to precipitation—part 2: bias‐correction and future projections. International Journal of Climatology, 34(11), 3282-3303. https://doi.org/10.1002/joc.3915
Scafetta, N. (2023). CMIP6 GCM ensemble members versus global surface temperatures. Climate Dynamics, 60(9-10), 3091-3120. https://doi.org/10.1007/s00382-022-06493-w
Sediqi, M. N., Hendrawan, V. S. A., & Komori, D. (2022). Climate projections over different climatic regions of Afghanistan under shared socioeconomic scenarios. Theoretical and Applied Climatology, 149(1-2), 511-524. https://doi.org/10.1007/s00704-022-04063-y
Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American statistical association, 63(324), 1379-1389. https://doi.org/10.1080/01621459.1968.10480934
Suryavanshi, S., Joshi, N., Maurya, H. K., Gupta, D., & Sharma, K. K. (2022). Understanding precipitation characteristics of Afghanistan at provincial scale. Theoretical and Applied Climatology, 150(3-4), 1775-1791. https://doi.org/10.1007/s00704-022-04257-4
Thrasher, B., Maurer, E. P., McKellar, C., & Duffy, P. B. (2012). Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrology and Earth System Sciences, 16(9), 3309-3314. https://doi.org/10.5194/hess-16-3309-2012
Weedon, G. P., Gomes, S., Viterbo, P., Shuttleworth, W. J., Blyth, E., Österle, H., ... & Best, M. (2011). Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. Journal of Hydrometeorology, 12(5), 823-848. https://doi.org/10.1175/2011JHM1369.1
Worku, G., Teferi, E., Bantider, A., & Dile, Y. T. (2020). Statistical bias correction of regional climate model simulations for climate change projection in the Jemma sub-basin, upper Blue Nile Basin of Ethiopia. Theoretical and Applied Climatology, 139, 1569-1588. https://doi.org/10.1007/s00704-019-03053-x
Xue, D., Lu, J., Leung, L. R., Teng, H., Song, F., Zhou, T., & Zhang, Y. (2023). Robust projection of East Asian summer monsoon rainfall based on dynamical modes of variability. Nature Communications, 14(1), 3856. https://doi.org/10.1038/s41467-023-39460-y
Yan, Y., You, Q., Wu, F., Pepin, N., & Kang, S. (2020). Surface mean temperature from the observational stations and multiple reanalyses over the Tibetan Plateau. Climate Dynamics, 55, 2405-2419. https://doi.org/10.1007/s00382-020-05386-0
Zarrin, A., & Dadashi-Roudbari, A. (2021). Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theoretical and Applied Climatology, 144, 643-660. https://doi.org/10.1007/s00704-021-03568-2
Zarrin, A., Dadashi-Roudbari, A., & Hassani, S. (2021). Historical variability and future changes in seasonal extreme temperature over Iran. Theoretical and Applied Climatology, 146, 1227-1248. https://doi.org/10.1007/s00704-021-03795-7
Zhai, J., Mondal, S. K., Fischer, T., Wang, Y., Su, B., Huang, J., ... & Uddin, M. J. (2020). Future drought characteristics through a multi-model ensemble from CMIP6 over South Asia. Atmospheric Research, 246, 105111. https://doi.org/10.1016/j.atmosres.2020.105111
CAPTCHA Image
Volume 11, Issue 1 - Serial Number 31
Native knowledge and Nature based solutions
June 2024
Pages 35-48
  • Receive Date: 22 December 2023
  • Revise Date: 17 April 2024
  • Accept Date: 20 April 2024