Prediction and comparison of Climate Changes in Mountainous and Palin Regions During 2010-2030 (Case Study: Yazd- Ardakan Watershed)

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Abstract

Introduction
It is expected that projected changes in the frequency and severity of extreme climate events, such as increased frequency of heat stress, droughts and flooding will have significant consequences on water resources. Increasing pressure on water resources due to climatic and anthropogenic changes as well as increasing competition among users is recognized challenges worldwide. Global scale studies identified the Mediterranean region as one of the most vulnerable regions to climatic and anthropogenic changes and thus as one of the world’s water crisis hot-spots. So investigation about this phenomenon in these areas will be very crucial. Yazd is located in arid regions and due to increasing population and industrial growth, has been highly regarded by planners and authorities. Water shortages in the past decades, lead to the water transfer of from Isfahan. But recently, due to increasing in rising water demand, the inadequacy of this project clearly felt. In the other hand, ground water resources of Yazd province are recharged from Shirkouh region. So due to this issue, to determine water stress risks in study area, considering climate change projections in shirkouh and Yazd as water supply and water consumption area is necessary.
Research Methodology
In the present study, to assess future climate changes in the study area, historical data from the Yazd and Dehbala stations -as water recharge and water depletion areas of Yazd-Ardakan aquifer-were analyzed by Lars software.
To do this, daily values of minimum and maximum temperature, precipitation, and sunshine were used. GCM model in this research is HadCM3. These data are derived from two scenarios, A2, A1B and B1 for 2010-2030 periods.
Validation of the predicted values was conducted using the statistical parameters, including bias, P Value of t Student statistics. To better analyze the results using Excel software, the moving average of predicted parameters for every month of the year was calculated for the 2010 to 2030 period and the corresponding graphs were drawn.
Discussions and Results
Results of two climate scenarios were evaluated and summarized below.
The results of statistical validation of the predicted values, showed no significant differences between historical and predicted values of precipitation, maximum temperature, minimum temperature and sunshine. Therefore, suitability of Lars model to simulate climatic data of the study area is confirmed. Investigation of the precipitation in the Dehbala station showed that in autumn season will be declining and in spring will be increasing. In other words, the distribution of precipitation in the future will have significant changes and as winter precipitation decreases the spring precipitation will have increasing trend. On the other hand, due to the significant decline in January precipitation it is expected that the proportion of precipitation falling as snow vs. rain decrease. Such a change would affect the hydrological response of the basins and increasing flooding in this season. Similar process will happen in plain area.
Based on the results, it can be said that in future, mountain stations has more variations in precipitation parameter than plain stations. These differences may be related to the amount of rainfall, in other words, in areas with higher precipitation, more variations in rainfall and rainfall distribution will be happen.
Results showed that in studying stations, monthly minimum and maximum temperature increasing in almost all months.
Conclusion
According to this study, recharge area of Yazd-Ardakan aquifer will experience climate change and changes in the type of precipitation. As a result, by increasing in rainfall to snow coefficient, there would be more flood and less aquifer recharge. In the other hand by increasing in maximum temperature in water consumption (plain) area in future, water needs will grow significantly. Therefore planners and authorities should consider this fact in future water resources allocation.

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References

آشفته، پ.س. و مساح بوانی، ع. ر.، 1391، بررسی تأثیر عدم قطعیت مدل‌های چرخۀ عمومی جو و اقیانوس(AOGCM) و سناریوهای انتشارگازهای گلخانه‌ای بر رواناب حوضه تحت تأثیرتغییر اقلیم، مطالعه موردی: حوضۀ قرنقو،آذربایجان شرقی، م. تحقیقات منابع آب ایران، سال هشتم، 2، 36-47.
بابائیان، ا. و نجفی نیک، ز.، 1385، ارزیابی تغییر اقلیم استان خراسان رضوی در دورۀ 2039–2010 میلادی، پروژه خاتمه یافته پژوهشکده اقلیم‌شناسی، گروه پژوهشی تغییر اقلیم.
بابائیان، ا.، نجفی نیک، ز.، زابل عباسی، ف .، حبیبی نوخندان، م.، ادب، ح. و ملبوسی، ش.، 1388، ارزیابی تغییر اقلیم کشور در دوره 2039–2010 با استفاده از ریزمقیاس‌نمایی داده‌های مدل گردش عمومی ECHO-G، م. جغرافیا و توسعه، 16، 135-152.
دوستی، م.، حبیب‌نژاد روشن، م.، شاهدی، ک. و میریعقوب زاده، م. ح.، 1392، بررسی شاخص‌های اقلیمی حوضه آبخیز تمر، استان گلستان درشرایط تغییر اقلیم با کاربرد مدل LARS-WG، م. فیزیک زمین و فضا، 39(4)، 177-189
روشن، غ. ر.، خوش اخلاق، ف. و عزیزی، ق.، 1391، آزمون مدل مناسب گردش عمومی جو برای پیش‌یابی مقادیر دما و بارش ایران، تحت شرایط گرمایش جهانی، جغرافیا و توسعه، 27(91)، 19-36.
عباسی، ف.، بابائیان، ا.، حبیبی نوخندان، م.، گلی مختاری، ل. و ملبوسی، ش.، 1389، ارزیابی تأثیر تغییر اقلیم بر دما و بارش ایران در دهه‌های آینده باکمک مدل MAGICC-SCENGEN، پژوهش‌های جغرافیای طبیعی، 72، 91-110.
کوچکی، ع. ر.، نصیری محلاتی، م.، سلطانی، ا.، شریفی، ح. ر.، کمالی، غ. ع. و رضوانی مقدم، پ.، 1382، شبیه‌سازی تغییرات آب و هوایی ایران در شرایط دو برابر شدن غلظت CO2 به وسیلۀ مدل‌های عمومی گردش، م. بیابان، 2، 178-191.
مهدی‌زاده، ص.، مفتاح هلقی، م.، سیدقاسمی، س. و مساعدی، ا.، 1390، بررسی تأثیرتغییراقلیم برمیزان بارش در حوضۀ سدگلستان، م. پژوهش‌های حفاظت آب وخاک، 18(3)،117-132.
 
Babaeian, I., Kwon, W. T. and Im, E. S., 2004, Application of weather generator technique for climate change assessment over Korea, Korea Meteorological Research Institute, Climate Research lab.
Bates, B. C., Kundzewicz, Z. W., Wu, S. and Palutikof, J. P., 2008, Eds. Climate Change and Water, Technical Paper VI of the Intergovernmental Panel on Climate Change, IPCC Secretariat, Geneva, 210 pp.
Eitzingera, J. tastna, M. SD. alud, ZD. and Dubrovsky, M., 2003, A simulation study of the effect of soil water balance and water stress on winter wheat production under different climate change scenarios, Agricultural Water Management. 61(3), 195-217.
Hertig, E. and Jacobeit, J., 2008, Downscaling future climate change, temperature scenarios for the Mediterranean area, Global and Planetary Change, 63.
Hewitson, B. C. and Crane, R. G., 1996, Climate downscaling: techniques and application, Clim. Res., 7, 85-95.
IPCC, Summary for policymakers in climate change, 2007, Impacts, adaptation and vulnerability. contribution of working group II to the 4th Assessment Report of the Intergovernmental Panel on Climate Change; Parry, M. L., Canziani, O. F., Palutikof, J. P., van der Linden, P. J., Hanson, C. E., Eds.; Cambridge University Press: Cambridge, UK, 2007, 7-22.
Kainuma, M. Matsuoka, Y. Morita, T. Masui, T. and Takahashi, K., 2004, Analysis of global warming stabilization scenarios: the Asian- Pacific integrated model, Energy Economics, 26(4), 709-719.
Khazaei, M. R., Zahabiyoun, B. and Saghafian, B., 2011, Assessment of climate change impact on floods using weather generator and continuous rainfall-Runoff model, International Journal of Climatology, Published online in Wiley Online Library, (wileyonlinelibrary.com) doi: 10.1002/joc.2416.
Kundzewicz, Z. W., Mata, L. J., Arnell, N. W., Döll, P., Kabat, P., Jiménez, B., Miller, K. A., Oki, T., Sen Z. and Shiklomanov, I. A., 2007, Freshwater resources and their management, in climate change: impacts, adaptation and vulnerability, Contribution of Working Group II to the 4th Assessment Report of the Intergovernmental Panel on Climate Change; Parry, M. L., Canziani, O. F., Palutikof, J. P., van der Linden, P. J., Hanson, C. E., Eds.; Cambridge University Press: Cambridge, UK, 2007, 173-210.
Mavromatis, T. and Hansen, J. W., 2001, Variability characteristics and simulated crop response of four stochastic weather generators. Agricultural and Forest Meteorology, 109, 2883-296.
Richard, S. and Lindzen, R., 1994, On the scientific basis for global warming scenarios, Environmental Pollution, 83(1-2), 125-134.
Semenov, M. A. and Barrow, E. M., 2002, LARS-WG a stochastic weather generator for use in climate impact studies. User’s manual, Version 3.0.
Semenov, M. A., Brooks, R. J., Barrow, E. M., and Richardson, C. W., 1998, Comparison of the WGEN and LARS-WG stochastic weather generators in divers climates, Climate Research 10.
Stern, N., 2006, Review of the economics of climate change, what is the economics of climate changes, Discussion Paper, Her Majesty’s Treasury, UK Government, 31 January.
Trenberth, K. E., Jones, P. D., Ambenje, P., Bojariu, R., Easterling, D., Klein Tank, A., Parker, D., Rahimzadeh, F., Renwick J. A. and Rusticucci, M., 2007, Observations: Surface and atmospheric climate change, in climate change 2007, the physical science basis, Contribution of Working Group I to the 4th Assessment Report of the Intergovernmental Panel on Climate Change; Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., Miller, H. L., Eds., Cambridge University Press, Cambridge, UK and New York, NY, USA, 2007.
Xu, Z. X., Chen, Y. N. and Li, J. Y., 2004, Impact of climate changes on water resources in the Tarim River basin, Water Resource Management, 18, 439-458.
Zarghami, M., Abdi, A., Babaeian, I., Hassanzadeh, Y. and Kanani, R., 2011, Impacts of climate change on runoffs in East Azerbaijan, Iran, Global and Planetary Change, 78, 137-146.
 
 

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