Applied Research in Geographical Sciences

Applied Research in Geographical Sciences

Modeling and predicting of the spatial variations Precipitation cores in Iran

Authors
1 Assistant Professor of climatology , Kharazmi University
2 PhD student of climatology, Kharazmi University, Tehran, Iran
Abstract
This research was carried out with the aim of understanding the spatial displacement of rainfall nuclei as an effective factor in the future hydrological conditions in Iran. Two types of databases were used to conduct this research. The first type of data is the monthly precipitation of 86 synoptic stations with the statistical period of 1986-1989 and the second type of predicted data from the output of the CCSM4 model under the three scenarios (RCP2.6, RCP4.5, and RCP6) from 2016 to 2036. After collecting and modeling the data, the maps were mapped to the ARCGIS environment. The results of the study showed that the terrestrial nuclei in the whole of Iran's zone in the four seasons will have changes with a negative trend in the future. The coefficient of rainfall variation in the spring, summer, autumn and winter seasons will be 61.4, 101.4, 58.9 and 55.8 percent, respectively. The results of the triple scenario study showed that the displacement of the spring core from all north north of the country to the northwest of the country is limited to the common borders of Iran, Turkey and Armenia (the Maku and Jolfa region), but in summer, the high core The northern shores and parts of the northwest of the country will be transported to the south of the country (around Khash and Saravan). In the autumn, the high-lying zone, which is located throughout the northern part of the country, will move to two distinct nuclei in the central Zagros (Dena and Zadkouh areas) and southwest Khazars (Anzali and Astara areas), and the core of winter from the central Zagros And the Caspian region will be transferred to the northwest of Kurdistan and southwest of West Azarbaijan, which will be seen in all scenarios. Another point is that, in addition to reducing the boulders, in the future, drought areas will cover more of the country.
Keywords

Alison, L. K, Richard, G. L, Nicholas, S. R. (2004). RCM rainfall for UK flood frequency estimation: Climate change results. Journal of Hydrology, 318(1), 163- 172.
Chiotti, Q.P., and Johston, T., (1995), Extending the Bound Arics of Climate Change Research, A Discussion on Agriculture, Journal of Rural Studies, 11:335-350.
Gagnon, S.; Singh, B.; Rousselle, J. and Roy, L. (2005). An Application of the Statistical Downscaling Model (SDSM) to Simulate Climatic Data for Stream Flow Modeling in Québec, Canadian Water Resources Journal, 30(4): 297–314.
Ghahreman, N, Tabatabaei, M, (2015), Feasibility of sugarcane cultivation during the next five decades under RCP climate change scenarios. (Case study: Khuzestan province, Iran), ICID 2015, Montpellier, France.
Harmsen, E, Miller, N. L,Schlegel, N. J, Gonzalez, J. E. (2009). Seasonal climate change impacts on evaporation, precipitation deficit and cop yield in Puerto Rico. Agricultural Water Management, 96(7), 1085-1095.
Hoar. T, Nychka.D. (2008), Statistical downscaling of the Community Climate System Model (CCSM) monthly temperature and precipitation projections, Instant research, pp1-8.
Hoffman.F & et al. (2006). Terrestrial biogeochemistry in the community climate system model (CCSM), Journal of Physics: Conference Series 46:363-369.
Kharin, V. V, Zwiers, F. W, Zhang, X, &Wehner, M. (2013). Changes in temperature and precipitation extremes in the CMIP5 ensemble. Climatic Change, 119(2), 345-357
Lee Titus, M, Sheng, J, Greatbatch, R, Folkins, I. (2013). Improving Statistical Downscaling of General Circulation Models, Atmosphere-Ocean, pp. 1–13
Marengo, J. A, Chou, S. C, Torres, R. R, Giarolla, A, Alves, L. M,&Lyra, A. (2014). Climate change in central and South America: Recent trends, future projections, and impacts on regional agriculture. Working Paper, No 73.
Manabe S, (1998), Study of global warming by GFDL climate models. Ambio 27(3): 182-186.
Moss, R. H, Edmonds, J. A, Hibbard, K. A, Manning, M. R, Rose, S. K, Van Vuuren, D. P, Wilbanks, T. J. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747-756.
Muhire, I, Ahmed, F. (2016). Spatiotemporal trends in mean temperatures and aridity index over Rwanda. Theoretical and Applied Climatology, 123(1-2), 399-414.
Pervez, Md, S, Geoffrey, M, Henebry, G.M. (2014). Projections of the Ganges–Brahmaputra precipitation Downscaled from GCM predictors, Journal of Hydrology, 517: 120–134.
Plattner, G. K, Stocker, T. F. (2010). From AR4 to AR5: New Scenarios in the IPCC Process. Workshop Report.
Souvignet, M, Gaese1. H, Ribbe, L, Kretschmer, N. and Oyarzún, R. (2010). Statistical downscaling of precipitation and temperature in north-central Chile: an assessment of possible climate change impacts in an arid Andean watershed, Hydrological Sciences Journal– Journal des Sciences Hydrology, 55: 41-57.
Taylor, K. E, Stouffer, R. J, Meehl, G. A. (2012). An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93(4), 485-498.
Van Vuuren, D. P, Edmonds, J, Kainuma, M, Riahi, K, Thomson, A, Hibbard, K., Rose. S. K. (2011). The representative concentration pathways: An overview. Climatic Change, 109, 5-31.
Van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K. & Rose, S. K. (2011). The representative concentration pathways: An overview. Climatic Change, 109, 5-31.
Zhang. X, Hogg, W. D, Bonsal, B. R. (2001). A cautionary note on the use of seasonally varying thresholds to assess temperature extremes: Comments on the use of indices to identify changes in climatic extremes'. Climatic Change, 50(4), 505-507.