Journal of Natural Environment

Predicting changes in climatic environmental factors in the Zagros region for the future periods

Document Type : Research Paper

Authors

1 Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 Rangeland and Watershed Management, Faculty of Natural Resources and Geosciences, Shahrekord University, Shahrekord , Iran

Abstract

Uncontrolled emissions of greenhouse gases into the atmosphere have been cited as a major reason and threat to climate change. Accordingly, it causes decreasing in precipitation, increasing of temperature, and the occurrence of extreme climatic consequences in the future. Due to the characteristics of communities and limitations, it has harmful consequences. Understanding the trend of these changes in global climate change projects is crucial to understanding future climate change. In this research, based on global atmospheric circulation models has been used a statistical model for regional downscaling. This study has been carried out in the North Kroon basin in order to study the trend of precipitation components and average temperature, to reveal the impact of influence on the region's climate, that the results have been shown in the forecast range of (2100 -2020). Using both optimistic and pessimistic scenarios (RCP2.6 and RCP8.5), the model evaluation performance for the precipitation variable (R = 0.85, MAD = 0.58, MSE = 0.73, RMSE = 0.84) and for the temperature variable (R = 0.93, MAD = 2.2, MSE = 1.5, RMSE = 1.3). All in all, the average precipitation will increase 35.97 mm compared to the observation period under RCP2.6 scenarios and will increase by 73.57 mm under RCP8.5 scenarios. Also, the temperature in both scenarios RCP2.6 and RCP8.5 will respectively increase by 3.01 and 4.79 ° C.

Keywords

Alimirzayi, Z., Zare Bidaki, R., Zamani Ahmad Mahmoudi, R., 2018, Meteorological and hydrological monitoring of North Karun watershed. Hydro Geomorphology 5(15), 115-133. (In Persian)
Anderson, H.E., Kronvang, B., Larsen, S.E., Hoffmann, C., Jensen, T.S., Rasmussen, E.K. 2006. Climate impacts on hydrology and nutrients in a Danish Lowland River basin, Science of the Total Environment 365, 223-237.
Arnell, N.W., Reynard, N.S., 1996. The effects of climate change due to global warming on river flows in Great Britain. Journal of Hydrology 183(3-4), 397-424.
Benestad, R.E., Hanssen-Bauer, I., Chen, D., 2007. Empirical Statistical Downscaling. World Scientific Publishing, Singapore. Change, I. P. O. C. (1990). Climate change: The IPCC scientific assessment. Mass, Cambridge. Christensen, Hewitson, J.H., Busuioc, B., Chen, A., Gao, A., Held, X.,I., Whetton, P., 2007. Regional climate projections. Chapter 11.
Chu, J.T., Xia, J., Xu C.Y., Singh, V.P., 2010. Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China. Theoretical and Applied Climatology 99, 149-161.
Gulacha, M.M., Mulungu, D.M., 2017. Generation of climate change scenarios for precipitation and temperature at local scales using SDSM in Wami-Ruvu River Basin Tanzania. Physics and Chemistry of the Earth, Parts A/B/C. Aug 1, 100:62-72.
Hasirchian, M., Zahabiun, B., Khazayi, M., 2018. Evaluating the performance of the SDSM model in investigating the effect of climate change on precipitation and temperature, Irrigation and Water Engineering 9(34), 108-120. (In Persian)
Lee, B.S., Park, J., Seo, J., Song, S.H., Kim, W., 2019. Delineation of agricultural drought-prone zones considering irrigation capacities of agricultural facilities under climate change. Paddy and Water Environment 17(4), 783-796.
Mahmood, R., Babel, M.S., 2014. Future changes in extreme temperature events using the statistical downscaling model (SDSM) in the trans-boundary region of the Jhelum River basin. Weather and Climate Extremes 5, 56-66. (In Persian)
Maurer, E.P., Hidalgo, H.G., 2008. Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods. Hydrology and Earth System Sciences 12(2), 551-563.
McAfee, S.A., Russell, J.L., Goodman, P.J., 2011. Evaluating IPCC AR4 cool-season precipitation simulations and projections for impacts assessment over North America. Climate Dynamics 37(11), 2271-2287.
Miao, C., Duan, Q., Sun, Q., Li, J., 2013. Evaluation and application of Bayesian multi-model estimation in temperature simulations. Progress in Physical Geography 37(6), 727-744.
Mohammadi, P., Malekian, A., Ghorbani, M., Nazari Samani, A., 2018. Investigation of changes of some climatic variables under future climate conditions in a semi-arid region. Desert Ecosystem Engineering 10(30), 65-80. (In Persian)
Najafi, M.R., Zwiers, F.W., Gillett, N.P. 2015. Attribution of Arctic temperature change to greenhouse-gas and aerosol influences. Nature Climate Change 5(3), 246-249.
Pachauri, R. K., Allen, M. R., Barros, V. R., Broome, J., Cramer, W., Christ, R., van Ypserle, J.P., 2014. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. 151 p.
Parry, M., Parry, M. L., Canziani, O., Palutikof, J., Van der Linden, P., Hanson, C. (Eds.). 2007. Climate change 2007-impacts, adaptation and vulnerability: Working group II contribution to the fourth assessment report of the IPCC (Vol. 4). Cambridge University Press.
Shirmohammadi, B., Malekian, A., Salajegheh, A., Taheri, B., Azarnivand, H., Malek, Z., Verburg, P.H. 2020. Impacts of future climate and land use change on water yield in a semiarid basin in Iran. Land Degradation & Development 31(10), 1252-1264.
Skaugen, T., Astrup, M., Roald, L.A.and Skaugen, T.E., 2002. Scenarios of extreme pre- cipitation of duration 1 and 5 days for Norway caused by climate change. Technical report. Norgesvassdrags-ogenergidirektorat, NVE, Postboks 5091 Majorstua, 0301 Oslo, Norway.
Van Vuuren, D.P., Edmonds, J.A., Kainuma, M., Riahi, K., Weyant, J., 2011. A special issue on the RCPs. Climatic Change 109(1), 1-4.
Wilby, R.L., Dawson, C.W., 2007. Statistical Downscaling Model SDSM Version 4.2. 94 p.
Wilby, R.L., Harris, I., 2006. A framework for assessing uncertainties in climate change impacts: low-flow scenarios for the River Thames, UK. Water Resources Research 42 (2).
Wilby, R.L., Hay, L.E., Gutowski, W.J., Arritt, R.W., Takle, E.S., Pan, Z., Leavesley, G.H., Clark, M.P., 2000. Hydrological responses to dynamically and statistically downscaled climate model output. Geophysical Research Letters 27, 1199-1202.
Zhang, L., Hickel, K., Dawes, W.R., Chiew, F.H.S., Western, A.W., Briggs, P.R. 2004. A rational function approach for estimating mean annual evapotranspiration. Water Resource Research 40, W02502.
Zhang, X.C., 2007. A comparison of explicit and implicit spatial downscaling of GCM output for soil erosion and crop production assessments. Climatic Change 84, 337-363.
Zhang, Y., You, Q., Chen, Ch., Ge, J. 2016. Impacts of climate change on stream flows under RCP scenarios: A case study in Xin River Basin, China. Atmospheric Research 178-179, 521-534.
 
Journal of Natural Environment
Volume 75, Issue 2 - Serial Number 2
September 2022
Pages 177-194