Projections of Temperature and Precipitation Changes under CMIP6 Scenarios in Sistan-va-Baluchestan Province

Document Type : Research Article

Authors

• S. Mirshekari ¹
• F. Yaghoubi ²
• S.A. Hashemi ³

¹ Agriculture Institute, Research Institute of Zabol, Zabol, Iran

² Department of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

³ Hamoun International Wetland Institute, Research Institute of Zabol, Zabol, Iran

https://doi.org/10.22067/jsw.2024.89164.1422

Abstract

Introduction
The 21st century is witnessing the increase of climate change as an important challenge due to its destructive environmental and socio-economic effects. Extreme climatic conditions have become frequent and more intense in recent decades as a result of human activities. Iran, as one of the countries in the Middle East with a different climate in each region of the country, has suffered significant adverse effects of climate change. Considering the importance of the climate change, it is important to investigate the changes in climate variables to know the future conditions and make management decisions. In the field of climate research, global climate models are useful tools that are often used to investigate the global climate system, including historical and projected periods. Since the use of the CMIP6 dataset provides improved clarity and accuracy for predicting future climate forecasts, the main objective of the present study is to predict the temperature and precipitation changes in the near, mid, and far future in Sistan-va-Baluchestan province.
 
Materials and Methods
The minimum temperature, maximum temperature, and precipitation data of 10 general circulation models (GCMs) of the 6th IPCC report for the baseline (1990-2014) were downloaded from the Global Climate Research Program database (https://esgf-node.llnl.gov). Then GCMs were including ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1-HR, CNRM-ESM2-1, EC-Earth3-CC, EC-Earth3-Veg-LR, INM-CM4-8, INM-CM5-0, MIROC6, and NorESM2-MM. Four statistical indicators including correlation coefficient (R²), RMSE, Nash-Sutcliffe efficiency (NSE), and mean absolute error (MAE) were used to evaluate the performance of 10 GCMs. Based on the results obtained from the these indicators, the models that had higher performance in predicting the temperature and precipitation data were selected as the best models for forecasting in the future. The ensemble of these models under two SSP2-4.5 and SSP5-8.5 scenarios for the near, middle, and far future (2026-2050, 2051-2075, and 2076-2100) were extracted from the World Climate Research Program database.
CMhyd (Climate Model data for hydrologic modeling) tool was used to bias correction climate data of the selected models. In order to choose the best bias correction method, the R², RMSE, NSE, and MAE were estimated.
After bias correction, the climate data of selected models were ensembled and then the changes in precipitation and maximum and minimum temperature in three future periods compared to the baseline was estimated.
 
Results and Discussion
The results showed that out of 10 GCMs, seven models had good performance (R² > 0.40, 4.23 < RMSE < 12.02°C, 0.12 < NSE < 0.74, and 3.36 < MAE < 9.59°C) in simulating daily minimum and maximum temperature. However, the performance of all models in simulated daily precipitation was poor (R² > 0.19, 1.24 < RMSE < 3.70 mm, -7.41 < NSE < -0.57, and 0.23 < MAE < 0.85 mm).
Among the different bias correction methods of temperature and precipitation available in CMhyd, the distribution mapping method had the best performance.
In all three regions, compared to the baseline, the average annual minimum and maximum temperature under two scenarios will increase in the future periods and precipitation will decrease in most periods and scenarios. These changes will be mainly in the SSP5-8.5 scenario compared to SSP2-4.5 and also in the far future period compared to the middle and near future. Averaged across all locations, annual maximum temperature showed increases in near, middle, and far projected periods of 1.3, 2.1, and 2.8°C under SSP2-4.5 and 1.6, 3.1, and 5.1°C under SSP5-8.5, respectively (Fig. 2), while for minimum temperature, the increases will be of 1.6, 2.6, and 3.4°C for SSP2-4.5 and 1.9, 3.9, and 6.3°C for SSP5-8.5. The range of annual precipitation among all sites was from –58.22 to 49.33% under SSP5-8.5 in the near and far future periods in Zabol and Iranshahr, respectively.
The annual increase in the average maximum and minimum temperature will be mainly due to the increase in air temperature in the months of January, February, August, September, October, November and December. The annual decrease in precipitation will mainly result from the decrease in precipitation in January, February, March, November, and December, and the annual increase in precipitation will result from the significant increase in precipitation in May and October compared to the baseline.
 
Conclusion
The results showed that under different scenarios of climate change, the maximum and minimum temperatures in the near, middle, and far future periods will face an increase compared to the baseline. However, the precipitation changes in the future time periods are not the same as compared to the baseline, and in some periods the precipitation will decrease and in others it will increase. But in general, the decrease in precipitation will be more than its increase. Therefore, it is very important to formulate and implement appropriate management programs for the needs of each region, in order to properly manage water resources and adapt to extreme temperatures and their consequences.

Keywords

• Bias correction
• Climate change
• CMhyd
• Maximum temperature
• Minimum temperature

Main Subjects

• Agricultural Meteorology

©2024 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

References

1. Abdolalizadeh, F., Mohammad Khorshiddoust, A., & Jahanbakhsh, S. (2023). Projection of the future outlook of temperature and precipitation in Urmia Lake basin by the CMIP6 models. Physical Geography Research, 55(1), 95-112. (In Persian with English abstract). https://doi.org/10.22059/jphgr.2023. 352727.1007737
2. Ashraf, B., Alizade, A., Mousavi Baygi, M., Bannayan Aval, M. (2013). Verification of temperature and precipitation simulated data by individual and ensemble performance of five AOGCM models for North East of Iran. Journal of Water and Soil, 28(2), 253-266. (In Persian with English abstract). https://doi.org/10.22067/ JSW.V0I0.38011.
3. Bağçaci, S.Ç., Yucel, I., Duzenli, E., & Yilmaz, M.T. (2021). Intercomparison of the expected change in the temperature and the precipitation retrieved from CMIP6 and CMIP5 climate projections: A Mediterranean hot spot case, Turkey. Atmospheric Research, 256, 105576. https://doi.org/10.1016/j.atmosres.2021.105576
4. Bararkhanpour Ahmadi, S., Nadi, M, Mazloom Babanari, S., & Jedariforoughi, A. (2024). Analyzing the effect of climate change on the trend of extreme temperatures along the coast of Mazandaran province based on CMIP6 models. Journal of Water and Soil Conservation, 30(4), 1-27. (In Persian with English abstract). https://doi.org/22069/jwsc.2023.21185.3635
5. Faisal, A.A., Taha, D.S., Hassan, W.H., Lakhera, S.K., Ansar, S., & Pradhan, S. (2023). Subsurface flow constructed wetlands for treating of simulated cadmium ions-wastewater with presence of Canna indica and Typha domingensis. Chemosphere, 338, 139469. https://doi.org/10.1016/j.chemosphere.2023.139469
6. GhorbaniMinaei, L., Mosaedi, A., Zakerinia, M., Kalbali, E., & Ghabaei Soogh, M. (2024). Study of future climate change on the temperature and precipitation trends in Qarasu basin based on the CMIP6 models. Iranian Journal of Soil and Water Research, 55(2), 245-268. (In Persian with English abstract)
7. Hamidianpour, M., & Hejazizadeh, Z. (2012). Investigating changes in temperature and precipitation using the atmospheric general circulation model, a case study of Sistan-va-Baluchestan National Congress on Border Cities and Security; Challenges and Strategies–CBCS, Sistan-va-Baluchestan University, Zahedan, Iran. (In Persian)
8. Hassan, W.H., Nile, B.K., Mahdi, K., Wesseling, J., & Ritsema, C. (2021). A feasibility assessment of potential artificial recharge for increasing agricultural areas in the Karbala desert in Iraq using numerical groundwater modeling. Water, 13(22), 3167. https://doi.org/10.3390/w13223167
9. IPCC. (2020). The physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland.
10. Kamworapan, S., Thao, P.T.B., Gheewala, S. H., Pimonsree, S., & Prueksakorn, K. (2021). Evaluation of CMIP6 GCMs for simulations of temperature over Thailand and nearby areas in the early 21st century. Heliyon, 7(11). https://doi.org/10.1016/j.heliyon.2021.e08263
11. Koocheki, A., Mahallati, M.N., Bannayan, M., & Yaghoubi, F. (2022). Simulating resilience of rainfed wheat–based cropping systems of Iran under future climate change. Mitigation and Adaptation Strategies for Global Change, 27(4), 27. https://doi.org/10.1007/s11027-022-09996-3
12. Kouhi, M., Mousavi Baygi, M., Farid hosseini, A.R., Sanaei Nejad, S.H., & Jabbari Nooghabi, H. (2012). Statistical downscaling of extremes of precipitation and construction of their future scenarios in the Kashfroud Basin. Journal of Climate Research, 1391(12), 35-53. (In Persian with English abstract)
13. Mianabadi, A., Bateni, M.M., & Mohammadi, S. (2023). Projection of change in the distribution of precipitation and temperature using bias-corrected simulations of CMIP6 climate models (Case study: Kerman synoptic station). Journal of Climate Change Research, 4(13), 65-84. (In Persian with English abstract)
14. Mohsen, K.A., Nile, B.K., & Hassan, W.H. (2020). Experimental work on improving the efficiency of storm networks using a new galley design filter bucket. In: IOP Conference Series: Materials Science and Engineering, vol. 671, No. 1. IOP Publishing, 012094.
15. Monteverde, C., De Sales, F., & Jones, C. (2022). Evaluation of the CMIP6 performance in simulating precipitation in the Amazon River basin. Climate, 10(8), 122. https://doi.org/10.3390/cli10080122
16. Muhaisen, N.K., Khayyun, T.S., Al Mukhtar, M., & Hassan, W.H. (2024). Forecasting changes in precipitation and temperatures of a regional watershed in Northern Iraq using LARS-WG model. Open Engineering, 14(1), 20220567.
17. Mukheef, R.A., Hassan, W.H., & Alquzweeni, S. (2024). Projections of temperature and precipitation trends using CMhyd under CMIP6 scenarios: A case study of Iraq's Middle and West. Atmospheric Research, 306, 107470. https://doi.org/10.1016/j.atmosres.2024.107470
18. Nash, J.E., & Sutcliffe, J.V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10(3), 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
19. Niazkar, M., Goodarzi, M.R., Fatehifar, A., & Abedi, M.J. (2023). Machine learning-based downscaling: application of multi-gene genetic programming for downscaling daily temperature at Dogonbadan, Iran, under CMIP6 scenarios. Theoretical and Applied Climatology, 151(1), 153-168.
20. Rashid, H., Yang, K., Zeng, A., Ju, S., Rashid, A., Guo, F., & Lan, S. (2021). Predicting the hydrological impacts of future climate change in a humid-subtropical watershed. Atmosphere, 13(1), 12. https://doi.org/10.3390/atmos 13010012
21. Rashidi Ghane, M., Motevalli, S., janbaz Ghobadi, G.R., & Kouhi, M. (2023). Evaluation of the ability of three statistical methods to downscale the output of temperature and precipitation of CMIP6 models in the Kashfrud basin. Journal of Climate Research, 1402(53), 117-132. (In Persian with English abstract)
22. Taylor, K.E. (2005). Summarizing multiple aspects of model performance in a single diagram. Journal Geophysics Research, 106, 7183-7192, http://wwwpcmdi.llnl.gov/publications/ab55.html
23. 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.
24. Van Wart, J., Grassini, P., Yang, H., Claessens, L., Jarvis, A., & Cassman, K.G. (2015). Creating long-term weather data from thin air for crop simulation modeling. Agricultural and Forest Meteorology, 209, 49-58. https://doi.org/ 10.1016/j.agrformet.2015.02.020
25. Willmott, C.J. (1981). On the validation of models. Physical Geography, 2(2), 184-194. https://doi.org/10.1080/ 02723646.1981.10642213
26. Yang, X., Zhou, B., Xu, Y., & Han, Z. (2021). CMIP6 evaluation and projection of temperature and precipitation over China. Advances in Atmospheric Sciences, 38, 817-830. https://doi.org/10.1007/s00376-021-0351-4
27. Yazdandoost, F., Moradian, S., Izadi, A., & Aghakouchak, A. (2021). Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison. Atmospheric Research, 250, 105369. https://doi.org/10.1016/j.atmosres.2020.105369
28. Zareian, M. (2022). Effects of climate change on temperature and precipitation in Yazd province based on combined output of CMIP6 models. JWSS-Isfahan University of Technology, 26(2), 91-105. (In Persian with English abstract)
29. Zarrin, A., & Dadashi-Roudbari, A. (2023). Evaluation of CMIP6 models in estimating temperature in Iran with emphasis on equilibrium climate sensitivity (ECS) and transient climate response (TCR). Iranian Journal of Geophysics, 17(1), 39-56. (In Persian with English abstract)
30. 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. https://doi.org/ 10.30499/ijg.2020. 249997.1292