سامانه پشتیبانی خدمات اطلاعات

  اخبار و اعلانات

 آمار

تعداد نشریات161
تعداد شماره‌ها6,532
تعداد مقالات70,502
تعداد مشاهده مقاله124,116,662
تعداد دریافت فایل اصل مقاله97,221,386
Jamali, Zahra, Khoorani, Asadollah. (1394). Impacts of climate change on extreme precipitation events in arid (Bandar Abbas) and semi-arid (Shahrekord) stations in Iran. , 1(1), 85-94.
Zahra Jamali; Asadollah Khoorani. "Impacts of climate change on extreme precipitation events in arid (Bandar Abbas) and semi-arid (Shahrekord) stations in Iran". , 1, 1, 1394, 85-94.
Jamali, Zahra, Khoorani, Asadollah. (1394). 'Impacts of climate change on extreme precipitation events in arid (Bandar Abbas) and semi-arid (Shahrekord) stations in Iran', , 1(1), pp. 85-94.
Jamali, Zahra, Khoorani, Asadollah. Impacts of climate change on extreme precipitation events in arid (Bandar Abbas) and semi-arid (Shahrekord) stations in Iran. , 1394; 1(1): 85-94.

Impacts of climate change on extreme precipitation events in arid (Bandar Abbas) and semi-arid (Shahrekord) stations in Iran

Natural Environment Change
مقاله 7، دوره 1، شماره 1، مهر 2015، صفحه 85-94    اصل مقاله (905.46 K)
نوع مقاله: Scientific and Research
نویسندگان
Zahra Jamali1؛ Asadollah Khoorani* 2
1MSc Graduated on Combatting Desertification, Hormozgan University, Bandar Abbas, Iran
2Assistance Professor, Geography Department, Hormozgan University, Bandar Abbas, Iran
چکیده
The aim of this paper is to project extreme precipitation events in an arid and a semiarid station. In order to project climate change based on general circulation models (GCMs), we have applied LARS-WG[1] downscaling tool. This stochastic weather generator down-scaled the climate of two synoptic stations using HADCM3 model and A2 emission scenario for 2040. We extracted extreme precipitation events, as daily 90th and 10th percentile for rainy days (considered if daily precipitation was greater than 1 mm), for based and projected data. The research outcomes showed an increase both in 90th percentile by 13 mm and in 10th percentile by 0.2 mm in arid station, Bandar Abbas. In the semiarid station, Shahrekord, the 90th percentile precipitation increased by 6.1 mm and the 10th percentile precipitation decreased by 3.4 mm. In total, for both stations, 90th percentile precipitations showed a more stable trend than the 10th percentile.



1. Long Ashton Research Station-Weather Generator
کلیدواژه‌ها
10th percentile؛ 90th percentile؛ Hadcm3؛ LARS_WG؛ RMSE
مراجع

1. Asakereh, H., 2012. Frequency Distribution Change of Extreme precipitation in Zanjan City. Geography and Environmental Planning Journal, vol. 45, No.1, 13-18 pp.

2. Benestad, S., Rasmus. E., 2006. Can we expect more extreme precipitation on the monthly time scale? Journal of Climate, Vol. 19: 630 637.

3. Becker. S., Hartmann. H., Zhsng. Q., Wu. Y., Tiang. T., 2007. Cyclicityanalysis of Precipitation regimes in the Yangtze River Basin, China. Int. J. Climatol. 28: 579–588.

4. Bartolini, G., Morabito, M., Crisci, A., Grifoni, D., Torrigianitonmaso, P., Martina, M.G., and Orlandini, S., 2008. Recent trends in orlandini. Simon 2008, Recent trends in indices of extremes. International Journal of Climatology. 28:1751-1760.

5. Alijani. B., O’Brien, J., Yarnal, B., 2007. Spatial analysis of precipitation intensity and concentration in Iran, Theor. Appl. Climatol. 94: 107-124.

6. Cryer, J.D., Chan, K.S., 2008. Time Series Analysis, Springer Texts in Statistics.

7. IPCC. 2007. Climate Change- the scientific basis. IntergovernmentalPanel on Climate Change. Cambridge University Press: Cambridge.

8. IPCC- TGCIA- Carter, T.R., Alfsen, K.E.B., Dai, P., Desanker, S.R., Gaffin, F., Giorgi, M., Hulme, M., Lal, L.J., Mata, L.O., Mearns, J.F.B., Mitchenll, T., Morit, R., Moss, D., Murdiyarson, J.D., Pabon-Caicedo, J., Palutikof, M.L., Parry, C., Rosenzweig, B., Seguin, R. Scholes, J., Whetton, P.H. 2007. Generate Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment, Intergovermental Panel on Climate Change, Task Group on Scenarios for Climate Impact Assessment, Version2: 66 pp.

9. IPCC, 2002. IPCC Workshop on Changes in Extreme Weather and Climate Events. Beijing, China, IPCC, 107 pp.

10. Racsko, P., Szeidl, L., Semenov, M., 1991. A serial approach to local stochastic weather models. Ecological Modeling 57, 27–41.

11. Rahimzadeh, F., Asgari, A. Fattahi, 2009. Variability of extreme temperature and precipitation in Iran during recent decades. Int. J. Climatol. 29: 329-343.

12. Semenov, M.A., Barrow, E.M. 1997. Use of a stochastic weather generator in the development of climate change scenarios. Climate Change 35, 397–414.

13. Sajjad Khan, M., Coulibaly, P., Dibike, Y., 2006. Uncertainty analysis of statistical downscaling methods. Journal of Hydrology 319, 357–382.

14. Semenov M.A., Brooks R.J., Barrow E.M., Richardson C.W. 1998. Comparison of the WGEN and LARS-WG stochastic weather generators in diverse climates. Climate Research10, 95-107.

15. Salon, S., Cossarini, G., Libralato, S., Gao, X., Solidoro, S., Giorgi, F., 2008. Downscaling experiment for the Venice lagoon. I. Validation of the present-day precipitation climatology. Clim Res 38:31–41.

16. SenRoy, Sh., Balling, J.R., Robert, C. 2004. Trends in extreme daily precipitation indices in India. Int. J. Climatol.24: 457-466.

17. Lane, M.E., kirshen P.H., Vogel R.M. 1999. Indicators of impact of global climate change on U.S water resources. ASCE, journal of Water Resource Planning and Management.125(4): 194-204.

18. Wilby, R.L., Wigley T.M.L., Conway D., Jones P.D., Hewitson B.C., Main J., Wilks D.S. 1998. Statistical downscaling of General Circulation Model output: A comparison of methods. Water Resources Research, 34, 2995–3008.

آمار
تعداد مشاهده مقاله: 2,359
تعداد دریافت فایل اصل مقاله: 1,516


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب