Future projection of climatic elements over Yazd province using RCP scenarios

Hosseini Khezr Abad, Azam Sadat; Vali, AbbasAli; Halabian, Amirhossein; Mokhtari, Mohammad Hossein

doi:10.22034/jargs.2023.385861.1015

Future projection of climatic elements over Yazd province using RCP scenarios

Document Type : Original Article

Authors

Azam Sadat Hosseini Khezr Abad ¹

AbbasAli Vali ²

Amirhossein Halabian ³

Mohammad Hossein Mokhtari ⁴

¹ Department Desert management and control, Factualy of Natural Resourcse and Earth Sciences, University of Kashan, Kashan, Iran.

² Department Desert management and control, Factualy of Natural Resourcse and Earth Sciences, University of Kashan, Kashan, Iran

³ Department of Geography, Payame Noor University, Tehran, Iran

⁴ Department Desert and Arid Land Management, Factualy of Natural Resources University of Yazd, Yazd, Iran.

10.22034/jargs.2023.385861.1015

Abstract

Aim: The aim of this research is the future projection of climatic elements over the northwest Yazd province using RCP scenarios.
Material & Method: This study uses simulating the climate variables, namely, precipitation and average temperature of Yazd province in the baseline period (2001-2020), by using the LARS-WG6.0 model and four models of CMIP5, for three possible emission scenarios (such as RCP 2.6, RCP 4.5, and RCP 8.5). By introducing the best model, it uses downscaling the climate variables for the future 2026-2055 and 2071- 2100.
Finding: According to the results, BCC-CSM1-1 and NorESM1-M models are introduced as the best models in projecting the mentioned variables with low error and acceptable NRMSE. The most significant increase in the temperature variable will occur in the period 2100-2071 and RCP8.5, so the temperature in the NorESM1-M model increases from 1.13 to 3.93 degrees Celsius in scenarios 2.6 and 8.5, respectively. The amount of rainfall in 2071-2100 will decrease compared to 2055-2026, and in some stations, it will increase or decrease compared to the average of the base period.
Conclusion: In future periods, the temperature will increase in the region and the greatest increase in RCP8.5. Precipitation in the future fluctuates so that it will have the most remarkable changes in RCP2.6.
Innovation: In this research, for future projection of climatic elements over the northwest Yazd province using GCM-CMIP5 models and RCP scenarios (RCP2.6, 4.5, and 8.5). After analyzing the error statistics, we chose BCC-CSM1-1 and NorESM1-M models to project future temperature and rainfall and better understand the region's climate. Therefore, the results of this study can be applied to regional planning, including meteorology, water resource management, and combating desertification.

Keywords

Yazd Province

Projection

Climate Change

Downscaling

Representative concentration pathway (RCP)

Ali Amedie, F. (2013). Impacts of Climate Change on Plant Growth, Ecosystem Services, Biodiversity and Potential Adaptation Measures. Master thesis in Atmospheric Science with Orientation towards Environmental Science (60 HEC), University of Gothenburg. https://studentportal.gu.se/digitalAssets/1432/1432197_fantahun.pdf

Almazroui, M., Nazrul Islam, M., Saeed, F., Alkhalaf, A.K. and Dambul, R. (2017). Assessing the robustness and uncertainties of projected changes in Temperature and Precipation in AR5 Global Climate Models Over the Arabian Peninsula. Atmospheric research, 194, 202-213. https://doi.org/10.1016/j.atmosres.2017.05.005

Arab solghar, A. A., Porhemat, J. and Goodarzi, M. (2022). Prediction of Climate Change using General Circulation Models and SDSM and LARS-WG Downscaling Models under RCP Scenarios in Dez Watershed, Physical Geography Quarterly, 14(55), 129-149. DOI:20.1001.1.20085656.1401.15.55.8.0. [in persian]

Ashraf, B., Mousavi Baygi, M., Kamali, G.A. and Davari. K. (2011). Prediction of Seasonal Variations of Climatological Parameters over Next 20 Years by Using Statistical Downscaling Method of HADCM3 Data (Case Study: Khorasan Razavi Province). Journal of Water and Soil, 25(4), 947-957. https://sid.ir/paper/460360/fa. [in persian]

Babaian, I., Karimian, M., Modirian, R. and Mirzaei, E. (2019). Prediction of the country climatic parameters by using the general circulation models of CMIP5 series for the period 2020-1100. NIVAR Scientific and Extension Journal, 43(104-105), 1-10. DOI:10.30467/nivar.2019.142745.1103. [in persian]

Bipal, K.J. and M. Mrinmoy. (2010). Impact of climate change on natural resource management. Springer Press, 495 pages. https://link.springer.com/book/10.1007/978-90-481-3581-3

Fallah Ghalhari, G. A., Yousefi. H., Hosseinzadeh. A., Alimardani. M. R. and Reyhani, E. (2019). Assessment of Climate Change in Bojnourd Station in 2016-2050 using Downscaling Models LARS WG and SDSM. Journal of Ecohydrology, 1(6), 99-109. DOI:10.22059/IJE.2018.265918.952. [in persian]

Goodarzi, M.R. and Fatehifar, A. (2022). Assessment of climate change effects on meteorological variables and maximum precipitation under new RCP emission in watershed. Human and environment, 20(61), 111-127. DOI: 20.1001.1.15625532.1401.20.2.9.0. [in persian]

Hejazi, S., Rezaei Moghaddam, M.H., Karami, F., Yarahmadi, J. and Bigham, A. (2022). Simulation and forecasting of some climatic variables by SDSM multiple linear model and RCP scenarios in Hajiler watershed. Journal of Geography and Environmental Hazards. DOI:10.22067/GEOEH.2022.75404.1206. [in persian]

Izadi, Z., Nasrollahi, A.H. and Haghighati Borujeni, B. (2019). Investigation of Rainfall and Air Temperature Changes Under Different Scenarios of Climate Change (Case study: Shahrekord). Water engineering, 6(1), 28-35. https://jwe.shoushtar.iau.ir/article_546143.html?lang=fa. [in persian]

kazemi, R. and Khazaei, M.R. (2022). Prediction of future climate change in Tehran and Yazd under RCPs scenarios and using LARS-WG model. Journal of Environmental Science and Technology (JEST). DOI: 10.22034/JEST.2021.48424.4865. [in persian]

Koutsovili, E., Tzoraki, O.,Theodossiou. N. and Gaganis, P. (2021). Numerical assessment of climate change impact on the hydrological regime of a small Mediterranean river, Lesvos Island, Greece. Acta hort regiotec, 1, 28–48. DOI: 10.2478/ahr-2021-0022

Liu, L., Liu, Z., Ren, X., Fischer, T. and Xu. Y. (2011). Hydrological impacts of climate change in the Yellow River Basin for the 21st century using hydrological model and statistical downscaling model. Quaternary International, 2, 211-220. https://doi.org/10.1016/j.quaint.2010.12.001

Mirakbari, M., Mesbahzadeh, T., Mohseni Sarvi, M., Khosravi, H. and Mortezaei Friz handi, Gh. (2018). Evaluating the model efficiency of CMIP5 series in simulating and predicting climatic parameters of rainfall, temperature and wind speed (Case study: Yazd province). Physical Geography Research, 50(3), 593-609.Mirakbari, M.میراکبری، م. DOI: 10.22059/JPHGR.2018.248177.1007156. [in persian]

Nourani, V., Hosseini Baghanam, H. and Gokcekus, H. (2018). Datadriven ensemble model to statistically downscale rainfall using nonlinear predictor screening approach. Journal of Hydrology, 565, 538–551. https://doi.org/10.1016/j.jhydrol.2018.08.049

Refsgaard, J.C., Arnbjerg-Nielsen, K., Drews, M., Halsnaes, K., Jeppesen, E., Madsen, H., Markandya, A., Olesen, J.E., Porter, J.R. and Christensen, J.H. (2013). The role of uncertainty in climate change adaptation strategies – A Danish water management example. Mitig Adapt Strateg Glob Change, 18, 337–359. DOI: 10.1007/s11027-012-9366-6

Semenov, M.A. and Barrow, E.M. (2002). A Stochastic Weather Generator for use in Climate, User Impact Studies. Version 3.0. User Manual. Atmospheric and Hydrologic Science, 28 pages.

Semenov. M.A., Donatelli, M., Stratonovitch, P., Chatzidaki, E. and Baruth, B. (2010). ELPIS: a dataset of local-scale daily climate scenarios for Europe. Climate Research, 44, 3-15. DOI: 10.3354/cr00865

Sharafati, A., Pezeshki, E., Shahid, S. and Motta, D. (2020). Quantifcation and uncertainty of the impact of climate change on river discharge and sediment yield in the Dehbar river basin in Iran. Journal of Soils and Sediments, 20, 2977–2996. DOI: 10.1007/s11368-020-02632-0

Sharma, D., Gupta, A.D. and Babel, M.S. (2007). Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand. Hydrology and Earth System Sciences, 11, 1373–1390. https://doi.org/10.5194/hess-11-1373-2007

Shrestha, A., Babel, M.S., Weesakul, S. and Vojinovic, Z. (2017). Developing Intensity Duration Frequency (IDF) Curves under Climate Change Uncertainty: The Case of Bangkok, Thailand. Journal of Water, 145, 1-22. https://doi.org/10.3390/w9020145

Sunyer, M.A., Madsen, H. and Ang, P.H. (2012). A comparison of diﬀerent regional climate models and statistical downscaling methods for extreme rainfall estimation under climate change. Journal of Atmospheric Research, 103,119–128. https://doi.org/10.1016/j.atmosres.2011.06.011

Tzoraki, O. (2020). Operating Small Hydropower Plants in Greece under Intermittent Flow Uncertainty: The Case of Tsiknias River (Lesvos). Challenges, 17, 1-15. https://ideas.repec.org/a/gam/jchals/v11y2020i2p17-d393807.html

Wu, C.H., Huang, G.R. and Yu, H.J. (2015). Prediction of extreme ﬂoods based on CMIP5 climate models: a case study in the Beijiang River basin, South China. Hydrology and Earth System Sciences, 19, 1385–1399. https://doi.org/10.5194/hess-19-1385-2015

Zhai, P., Roberts, D. and Shukla, P.R. (2018). Summary for Policymakers. In: Global warming of 1.5°C. An IPCC Special Report. ResearchGat, 1, 1-33. https://www.ipcc.ch/site/assets/uploads/2018/11/SR1.5_SPM_Low_Res.pdf