بررسی هم‌زمان عدم قطعیت های نوسانات اقلیمی و سناریوهای انتشار در ارزیابی اثرات تغییر اقلیم آینده بر دما و بارش زنجان
محورهای موضوعی :
تحلیل ریسک زیست محیطی
محمد رضا خزائی
1
,
مطلب بایزیدی
2
,
ایمان باباییان
3
1 - استادیار، گروه مهندسی عمران، دانشگاه پیام نور، ایران.
2 - استادیار، گروه مهندسی آب، واحد سنندج، دانشگاه آزاد اسلامی، سنندج، ایران.
3 - استادیار، پژوهشکده اقلیم شناسی، سازمان هواشناسی کشور.
تاریخ دریافت : 1394/06/07
تاریخ پذیرش : 1394/12/22
تاریخ انتشار : 1398/10/01
کلید واژه:
تغییر اقلیم,
LARS-WG,
عدم قطعیت,
نوسانات اقلیم,
استوکاستیک,
چکیده مقاله :
زمینه و هدف: در این تحقیق آثار تغییر اقلیم آینده زنجان بر متغیرهای بارش روزانه، کمینه دمای روزانه و بیشینه دمای روزانه، با تحلیل عدم قطعیت های نوسانات طبیعی اقلیم و سناریوهای انتشار، ارزیابی شده است. با منظور نمودن این عدم قطعیت ها، نتایج دامنه وسیعی از حالات محتمل آینده را در بر می گیرد که در افزایش قابلیت اعتماد نتایج بسیار مهم است.
روش بررسی: برای کاهش مقیاس سناریوهای آینده از مدل استوکستیک LARS-WG استفاده شده است. خروجی های مدل CGCM3 برای زنجان ریز مقیاس شده است. تحلیل عدم قطعیت سناریوهای انتشار، با مقایسه ی نتایج برای سه سناریوی A1B، A2، و B1 که به ترتیب بیان گر حالات غلظت متوسط، زیاد، و کم گازهای گل خانه ای هستند، انجام شده است. تحلیل عدم قطعیت نوسانات اقلیمی با مقایسه حدود 90% تغییرات 100 سری 30 ساله ی تولید شده توسط مدل LARS-WG برای اقلیم حال و برای هر سناریوی انتشار اقلیم آینده انجام شده است.
یافته ها: نتایج حاکی از افزایش قابل توجه میانگین های کمینه دمای روزانه و بیشینه دمای روزانه در اقلیم آینده است. علی رغم نوسانات اقلیم، پیش یابی می شود که در همه ماه های سال میانگین های بیشنه دمای روزانه و کمینه دمای روزانه افزایش یابد. به علاوه، عدم قطعیت سناریوهای انتشار در مقایسه با میزان افزایش دما اندک است. هم چنین در اغلب ماه های سال انتظار می رود که مقدار بارش اقلیم آینده کاهش یابد، اما به دلیل نوسانات اقلیمی، افزایش مقدار بارش نیز با احتمال اندک ممکن است.
بحث و نتیجه گیری: متغیرهای دما و بارش اقلیم آینده زنجان نسبت به اقلیم فعلی تغییر خواهد داشت و عدم قطعیت های نوسانات طبیعی اقلیم و سناریوهای انتشار مهم است و لازم است که مورد توجه قرار گیرد.
چکیده انگلیسی:
Background and Objective: In this paper climate change impacts on daily precipitation, daily maximum, and daily minimum temperature are estimated, while joint uncertainties due to natural climate variability and emission scenarios are estimated. By considering these uncertainties, the results incorporate a wide range of future possible situations which is a great importance in increasing the reliability of the results.
Method: For downscaling of future GCM scenarios, Weather Generator method is used using LARS-WG model. CGCM3 outputs for based on three emissions scenarios, medium (A1B) and high (A2), low (B1), are downscaled for Zanjan. Uncertainty due to natural climate variability is estimated by comparison of 90% limits of 100 LARS-WG generated series for historic and for each future climate scenarios.
Findings: The results of this research show that the daily minimum and maximum temperature will increase in the future. Despite the Uncertainty due to natural climate variability, if is expected that the monthly means of daily minimum and maximum temperature will increase for the entire calendar months. Moreover, the uncertainty of emission scenarios is low in comparison with the future increase in temperature. It is expected that the average of monthly precipitation will decrease for the most of the calendar months; although, there is a little possibility for increase in precipitation due to natural climate variability.
Discussion and Conclusion: In result of climate change, temperature and precipitation of the Zanjan will change in the future and uncertainties due to natural climate variability and emission scenarios are important in climate change impact assessment on precipitation and temperature.
منابع و مأخذ:
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Minville M, Brissette F, Leconte R., 2008. Uncertainty of the impact of climate change on the hydrology of a nordic watershed, Journal of Hydrology 358:70-83.
Khazaei M.R., Ahmadi S., Saghafian B., Zahabiyoun B., 2013. A new daily weather generator to preserve extremes and low-frequency variability, Climatic Change: 119:631–645.
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Prudhomme, C., Reynard, N. & Crooks, S., 2002. Downscaling of global climate models for flood frequency analysis: where are we now?, Hydrological Processes, 16, 1137-1150.
Fowler HJ, Blenkinsop S, Tebaldi C. 2007. Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modeling, International Journal of Climatology 27:1547-1578.
Wilby, R.L., Dawson, C.W. & Barrow, E.M., 2002. SDSM - a decision support tool for the assessment of regional climate change impacts, Environmental Modelling & Software, 17, 147-159.
Semenov, M.A., and Brooks, R.J., 1998. Comparison of the WGEN and LARSWGstochastic weather generators for diverse climates, Climate Research,10: 95-107.
Utset, A., Martinez-cob, A., Farre, I. & Cavero, J., 2006. Simulating the effects of extreme dry and wet years on the water use of flooding-irrigated maize in a Mediterranean landplane, Agricultural Water Management, 85, 77-84.
Dibike, Y. B. & Coulibaly, P., 2005. Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models, Journal of Hydrology, 307, 145-163.
Mavromatis, T. & Hansen, J.W., 2001. Interannual variability characteristics and simulated crop response of four stochastic weather generators, Agricultural and Forest Meteorology, 109, 283-296.
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Semenov, M. A., 2007. Development of high-resolution UKCIP02-based climate change scenarios in the UK, Agricultural and Forest Meteorology, 144, 127-138.
Holman, I.P., Tascone, D. & Hess, T.M., 2009. A comparison of stochastic and deterministic downscaling methods for modelling potential groundwater recharge under climate change in East Anglia, UK: implications for groundwater resource management, Hydrogeology Journal, 17, 1629-1641.
Kilsby, C. G., Jones, P.D., Burton, A., Ford, A. C., Fowler, H.J., Harpham, C., James, P., Smith, A. & Wilby, R.L. 2007, A daily weather generator for use in climate change studies, Environmental Modelling & Software, 22, 1705-1719.
Dubrovsky, M., Buchtele, J. & Zalud, Z., 2004. High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modeling, Climatic Change, 63, 145-179.
Hansen, J.W., Mavromatis, T., 2001. Correcting low-frequency variability bias in stochastic weather generators, Agricultural and Forest Meteorology 109:297-310.
_||_
IPCC, 2001. Climate change 2001. Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the third assessment report of the Intergovernmental Panel on Climate Change. UK: Cambridge University Press.
IPCC, 1995. Impacts, Adaptations and Mitigation of Climate Change: Scientific-Technical Analyses. In: WATSON, R. T., ZINYOWERA, M.C., MOSS, R.H. (ed.). UK.
Khazaei, M.R., Zahabiyoun, B., and Saghafian, B., 2012. Assessment of climate change impact on floods using weather generator and continuous rainfall-runoff model, International Journal of Climatology, 32:1997-2006
Khazaei, M.R., Climate Change Impacts assessment on Floods Frequency and Magnitude, Ph.D. thesis, Iran University of Science and Technology, School of Civil Engineering, 2010, 187 pages. (In Persian)
Babaeian, E., Nagafineik, Z., Zabolabasi, F., Habeibei, M., Adab, H., malbisei, S., 2010. Climate Change Assessment over Iran During 2010-2039 by Using Statistical Downscaling of ECHO- G Model, Geography and Development Iranian Journal, 16:135-152. (In Persian)
Ghorbanizadeh Karazi, H., Chelehmal Dezfoulnezhad, M., 2010. Study on the effect of climate change on snowmelt runoff timing in Dez basin. Journal of Wetland Ecobiology, 2: 56-66. (In Persian)
Kamal, A.R., Masah Bavani, A.R., 2011. Climate change and variability impact in basin’s runoff with interference of tow hydrology models uncertainty. Journal of Water and Soil, 24:920-931. (In Persian)
Heidari, A.R., Assessment of various climate change scenarios on multi objective reservoirs operation (Case study: Ami Kabir Dam), Master science thesis, Tarbiat Modares University, School of Civil Engineering, 2011, 125 pages. (In Persian)
IPCC, 2007. General Guidelines on the use of Scenario Data for Climate Impact and Adaptation Assessment, version 2.
Kay, A. L., Davies, H. N., Bell, V. A. & Jones, R. G., 2009. Comparison of uncertainty sources for climate change impacts: flood frequency in England, Climatic Change, 92, 41-63.
Reaney, S. M.; Fowler, H. J., 2008. Uncertainty estimation of climate change impacts on river flow incorporating stochastic downscaling and hydrological model parameterisation error sources, BHS 10th National Hydrology Symposium, Exeter.
Minville M, Brissette F, Leconte R., 2008. Uncertainty of the impact of climate change on the hydrology of a nordic watershed, Journal of Hydrology 358:70-83.
Khazaei M.R., Ahmadi S., Saghafian B., Zahabiyoun B., 2013. A new daily weather generator to preserve extremes and low-frequency variability, Climatic Change: 119:631–645.
Prudhomme, C., Jakob, D. & Svensson, C., 2003. Uncertainty and climate change impact on the flood regime of small UK catchments, Journal of Hydrology, 277, 1-23.
Wilby RL, Christian WD., 2007. SDSM 4.1- a decision support tool for the assessment of regional climate change impacts, User Manual. http://co-public.lboro.ac.uk/cocwd/SDSM/SDSMManual.pdf
Prudhomme, C., Reynard, N. & Crooks, S., 2002. Downscaling of global climate models for flood frequency analysis: where are we now?, Hydrological Processes, 16, 1137-1150.
Fowler HJ, Blenkinsop S, Tebaldi C. 2007. Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modeling, International Journal of Climatology 27:1547-1578.
Wilby, R.L., Dawson, C.W. & Barrow, E.M., 2002. SDSM - a decision support tool for the assessment of regional climate change impacts, Environmental Modelling & Software, 17, 147-159.
Semenov, M.A., and Brooks, R.J., 1998. Comparison of the WGEN and LARSWGstochastic weather generators for diverse climates, Climate Research,10: 95-107.
Utset, A., Martinez-cob, A., Farre, I. & Cavero, J., 2006. Simulating the effects of extreme dry and wet years on the water use of flooding-irrigated maize in a Mediterranean landplane, Agricultural Water Management, 85, 77-84.
Dibike, Y. B. & Coulibaly, P., 2005. Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models, Journal of Hydrology, 307, 145-163.
Mavromatis, T. & Hansen, J.W., 2001. Interannual variability characteristics and simulated crop response of four stochastic weather generators, Agricultural and Forest Meteorology, 109, 283-296.
Khan, M. S., Coulibaly, P. & Dibike, Y., 2006. Uncertainty analysis of statistical downscaling methods. Journal of Hydrology, 319, 357-382.
Semenov, M. A., 2007. Development of high-resolution UKCIP02-based climate change scenarios in the UK, Agricultural and Forest Meteorology, 144, 127-138.
Holman, I.P., Tascone, D. & Hess, T.M., 2009. A comparison of stochastic and deterministic downscaling methods for modelling potential groundwater recharge under climate change in East Anglia, UK: implications for groundwater resource management, Hydrogeology Journal, 17, 1629-1641.
Kilsby, C. G., Jones, P.D., Burton, A., Ford, A. C., Fowler, H.J., Harpham, C., James, P., Smith, A. & Wilby, R.L. 2007, A daily weather generator for use in climate change studies, Environmental Modelling & Software, 22, 1705-1719.
Dubrovsky, M., Buchtele, J. & Zalud, Z., 2004. High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modeling, Climatic Change, 63, 145-179.
Hansen, J.W., Mavromatis, T., 2001. Correcting low-frequency variability bias in stochastic weather generators, Agricultural and Forest Meteorology 109:297-310.
