تحلیل فضایی مخاطرات محیطی

تحلیل فضایی مخاطرات محیطی

شبیه‌سازی اثر گرمایش جهانی بر میانگین و رخدادهای حدی برخی متغیرهای هیدرواقلیمی در حوضه ی آبریز شاندیز، مطالعه موردی: مدل گردش کلی CanESM2

نویسندگان
1 دانشگاه حکیم سبزواری
2 مشهد-پژوهشکده اقلیم شناسی-
چکیده
دو مشخصه بارز اقلیم آینده تغییر در میانگین و مقادیر حدی متغیرهای هیدرواقلیمی می‌باشد، از این رو شبیه سازی رفتار اقلیم حوضه آبریز شاندیز که یک منطقه گردشگری مهم در شمال‌شرق کشور است در دهه های آینده نقش مهمی در شناخت وضعیت اقلیم و آسیب پذیری احتمالی این مناطقه از تغییر اقلیم دارد. هدف از این پژوهش شناسایی مقادیر حدی دما، بارش و تغییرات رواناب حوضه آبریز شاندیز و مقایسه شرایط پایه و آینده است. برای نیل به این هدف از آمار روزانه دما و بارش روزانه 30 سال آماری (از 1990-1961) ایستگاه سینوپتیک مشهد استفاده شده است. همچنین برای پیش بینی بارش، دمای حداقل و حداکثر در آینده از داده های مدل گردش کلی CanESM2 تحت سه سناریوی انتشار RCP2.6، RCP4.5 و RCP8.5 برای دوره 2100-2041 استفاده شده است. برای ریزگردانی خروجی مدل CanESM2 از روش آماری SDSM و برای استخراج مقادیر حدی بارش از نرم افزار RClimDex استفاده شده است. نتایج نشان داد که در دوره آینده نه تنها در مقدار بارش ایستگاه مشهد بلکه در الگوی بارش نیز تغییراتی رخ خواهد داد. بر اساس نتایج بدست آمده، بارش سالانه در دهه ی 2070-2041، بین 37 تا 54 درصد نسبت به دوره ی دیدبانی افزایش می یابد، و میزان افزایش بارش دهه ی 2100-2071 بین 52 تا 66 درصد افزایش می یابد. تعداد رخداد بارش های روزانه با شدت های 10، 20 و 30 میلیمتر در روز، بارش های با آستانه های صدک 95 و 99 دوره های آتی در تمامی فصول ایستگاه مشهد نسبت به دوره مشاهداتی (1990-1961) افزایش خواهند یافت. در دهه های آینده میانگین دمای حداکثر مشهد نسبت به دوره مشاهداتی بین 4/6 – 6/0 درجه سلسیوس و میانگین دمای حداقل بین 5/1 تا 2/4 درجه سلسیوس افزایش خواهد یافت.

از مدل SWAT جهت ارزیابی اثرات تغییراقلیم بر میزان رواناب حوضه استفاده گردید. بدین منظور ابتدا این مدل با استفاده از دبی ایستگاه هیدرومتری شاندیز برای دوره 2012-2003، واسنجی و اعتبارسنجی شد که مقادیر R2 به ترتیب 65/0 و 52/0 بدست آمد. در ادامه با بکارگیری داده‌های ریزمقیاس شده مدل CanESM2 در مدل SWAT، تغییرات رواناب خروجی از حوضه طی دوره های 2070-2041 و 2100-2071 شبیه سازی گردید. اعمال نتایج تغییرات بارش و دمای حوضه در دهه های آینده بر مدل SWAT نشان داد که دبی حوضه شاندیز در دهه های آینده بین 2 تا 104 درصد افزایش خواهد یافت.
کلیدواژه‌ها

عنوان مقاله English

Simulation of the effect of global warming on the mean and extreme events of some hydrochemical variables in Shandiz catchment basin Case study: The Case of the general circulation model CanESM2

نویسندگان English

Elham Fahiminezhad 1
M ohammag Baaghide 1
Iman Babaeian 2
Alireza Entezari 1
1 Hakim Sabzevari University
2 Climatological Research Institute, National Center of Climatology, Research Group Climate Change
چکیده English

Changes in the mean and the extreme values of hydroclimatic variables are two

prominent features of the future climate. Therefore, simulating the climatic

behavior of Shandiz catchment area, an important tourist area in the northeast of

the country, will play an important role in identifying the climate condition and

potential vulnerability of these areas in the coming decades of climate change.

In this study, we will

evaluate the effects of climate change on extreme values of the basin micro scaling

precipitation and temperature in CanESM2 model using SDSM model and

simulating runoff with SWAT model in future decades.

To achieve this goal, the daily temperature and precipitation statistics of the 30

statistical years (1961-1990) of Mashhad synoptic station have been

used. The data of the CanESM2 general circulation model under RCP2.6, RCP4.5

and RCP8.5 scenarios are also used to predict precipitation, the minimum and

maximum temperature for 2041 to 2100.

According to the results, the annual precipitation rises 37 to 54 percent from 2041

to2070 compared to the observation period, and the increase in rainfall of the

2071-2100 rises 52 to 66 percent. Precipitation extreme values, the mean of

maximum and minimum temperatures in future periods in all seasons of Mashhad

station will increase compared to the observation period (1961-1990).In future decades, the average maximum temperature in Mashhad will increase from 4.6 to 0.65 degrees Celsius

and the average minimum temperature will increase 53/1 to 22/4.

By introducing micro scaled time series of the maximum temperature, temperature,

and micro scaled precipitation by SDSM model to SWAT model, the monthly time

series of Shandiz watershed runoff at Sarasiab Station was simulated for the two

periods of 2041-2070 and 2071-2100 under three distribution scenarios of RCP2.6,

RCP4.5 and RCP8.5. For this purpose, first, the model was calibrated and validated

using Shandiz hydrometric station runoff for 2003-2012, and the values of R2 were

65 and 52, respectively. Subsequently, with the introduction of micro scaled time

series of maximum and minimum temperatures, and micro scaled precipitation by

SDSM model to SWAT model, the average annual trend shows that runoff

increases in the coming decades. The lowest average annual increase for runoff is

in 2041-2070 and RCP4.5 scenario, with an increase of 56.1% over the observation

period. The highest increase of average annual monthly runoff is from 2071 to2100

under RCP 2.6 scenario with 53% to 104% runoff compared to the observation period.




کلیدواژه‌ها English

Global warming
Hydroclimatic Risks
GCM Model
SDSM
SWAT
Shandiz Basin
- Abbaspour, K.C. 2009. User manual for SWAT-CUP2, SWAT calibration and uncertainty analysis programs. Swis Federal Institute of Aquatic Science and Technology, Eawag, Duebendorf, Switzerland, 95 pages.
- Alexander, L.V.; X. Zhang, T.C. Peterson, J. Caesar, B. Gleason, A.M.G. Klein Tank, and A. Tagipour. 2006. Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research: Atmospheres, 111(D5.
- Badrul, M.M.; P. Soni, S. Shrestha, and N.K. Tripathi. 2016. Changes in Climate Extremes over North Thailand, 1960–2099. Journal of Climatology, 2016: 18-33.
- Bell, J.L.; L.C. Sloan, and M.A. Snyder. 2004. Regional changes in extreme climatic events: A future climate scenario. Journal of Climate. 17(1): 81-87.
- Beniston, M.; D.B. Stephenson, O.B. Christensen, C.A. Ferro, C. Frei. S. Goyette, and K. Woth. 2007. Future extreme events in European climate: An exploration of regional climate model projections. Climatic Change, 81(1): 71-95.
- Brunetti, M.; L. Buffoni, F. Mangianti, M. Maugeri, and T. Nanni, T. 2004. Temperature, precipitation and extreme events during the last century in Italy. Global and Planetary Change, 40(1): 141-149.
- Chamchati, H.; and M. Bahir. 2011. Contribution of climate change on water resources in semi-arid areas: Example of the Essaouita Basin (Morocco). American Journal of Scientific and Industrial Research. 2(2): 209-215.
- Cheema, S.B.; G. Rasul, G. Ali, D.H. Kazmi D.H. 2013. A Comparison of Minimum Temperature Trends with Model Projections. Pakistan Journal of Meteorology. 8: 39-52.
- Collins, M.; R. Knutti, J.M. Arblaster, J.L. Dufresne, T. Fichefet, P. Friedlingstein, and M. Wehner. 2013. Chapter 12 - Long-term climate change: Projections, commitments and irreversibility. In: Climate Change 2013: The Physical Science Basis. IPCC Working Group I Contribution to AR5. Eds. IPCC, Cambridge: Cambridge University Press.
- Deryng, D.; D. Conway, N. Ramankutty, J. Price, and R. Warren. 2014. Global crop yield response to extreme heat stress under multiple climate change futures. Environmental Research Letters, 9(3): 034011.
- Easterling, D.R., Meehl, G.A., Parmesan, C., Changnon, S.A., Karl, T.R., Mearns, L.O. (2000), Climate extremes: Observations, modeling, and impacts. Science, 289(5487): pp. 2068-2074.
- Fan, L., Xiong, Z. (2015), Using quantile regression to detect relationships between large-scale predictors and local precipitation over northern China, Advances in Atmospheric Sciences, 32(4), pp 541-552.
- Faramarzi, M.; K.C. Abbaspour, R. Schulin, and H. Yang, 2009. Modelling blue and green water resources availability in Iran. Hydrolgical Processes. 23: 486–501.
- Folland, C. K.; T.R. Karl, and M. Jim Salinger. 2002. Observed climate variability and change. Weather, 57(8): 269-278.
- IPCC. 2007. Summary for Policymakers in Climate Change, The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge. PP. 1-18.
- Intergovernmental Panel on Climate Change (IPCC). Summary for Policymakers. In ClimateChange2013: The Physical Science Basis; Contribution of Working Group I to the IPCC Fifth Assessment Report Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013.
- IPCC. 2014. Annex II: glossary. K. J. Mach, S. Planton, C. von Stechow (Eds.), 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. In: Core Writing Team, Pachauri RK, Meyer LA (Eds.), IPCC, Geneva, Switzerland, pp. 117-130.
- Ha, K. J.; and K. S. Yun. 2012. Climate change effects on tropical night days in Seoul, Korea. Theoretical and Applied Climatology. 109(1-2): 191-203.
- Liu, J.; S. Fritz, C. F. A. Van Wesenbeeck, M. Fuchs, L. You, M. Obersteiner, and H. Yang. 2008. A spatially explicit assessment of current and future hotspots of hunger in Sub-Saharan Africa in the context of global change. Global and Planetary Change. 64(3): 222-235.
- Hoogwijk , M., Faaij, A., de Vries, B. and Turkenburg, W. 2009. Exploration of Regional and Global Cost-Supply Curves of Biomass Energy from Shortrotion Crops at Abandoned Cropland and Rest Land under Four IPCC SRES Land-use Senarios, Biomass & Bioenergy, 33: 26-43.
- Kharin, V. V.; F. W. Zwiers, X. Zhang, and M. Wehner, 2013. Changes in temperature and precipitation extremes in the CMIP5 ensemble. Climatic Change, 119(2): 345-357.
- Klein T.; F. W. Zwiers, X. Zhang. 2009. Guideline on analysis of extremes in a changing climate in support of inform ed decisions for adaptation, WMO Publication, 72: 55pp.
- Klein, T,; A. M. G. Wijngaard, J. B., Können, G. P., Böhm, R., Demarée, G., A. Gocheva, and R. Heino. 2002. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. International journal of climatology, 22(12):1441-1453.
- Kundzewicz Z.W., Mata L.J., Arnell N.W., D¨oll P., Kabat P., Jim´enez B., Miller K.A., Oki T., Sen Z. and Shiklomanov I.A. 2007. Freshwater resources and theirmanagement. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Parry, M. L. Canziani O. F. Palutikof J. P. van der Linden P. J. and Hanson C. E. Cambridge University Press, Cambridge, UK, 173–210, 2007.
- Marengo, J. A.; S. C. Chou, R. R. Torres, A. Giarolla, L. M. Alves, and A. Lyra. 2014. Climate change in central and South America: Recent trends, future projections, and impacts on regional agriculture. Working Paper, No 73.
- Meehl, G. A.; F. Zwiers, J. Evans, T. Knutson, L. Mearns, and P. Whetton. 2000. Trends in extreme weather and climate events: Issues related to modeling extremes in projections of future climate change. Bulletin of the American Meteorological Society. 81(3): 427-436.
- Merritt W.S., Alila Y., Barton M., Taylor B., Cohen S. and Neilsen D. 2006. Hydrologic response to scenarios of climate change in subwatersheds of the Okanagan basin, British Columbia. Journal of Hydrology 326, 79-108.
- Molina E.; D. Trolle, S. Martinez, and A. Sastre. 2014. Hydrological and Water quality impact assessment of a Mediterranean Limon-reservoir under climate change and land use management Scenarios. Hydrology, 509: 354-366.
- Moriasi, D. N.; J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, and T. L. Veith. 2007. Model evaluation guideline for systematic quantification of accuracy in watershed simulation. American Society of Agricultural and Biological Engineers ISSN 0001−2351 Transactions of the ASABE 50(3): 885-900.
- Moss, R. H.; J. A. Edmonds, K. A. Hibbard, M. R. Manning, S. K. Rose, D. P. Van Vuuren, and T. J. Wilbanks. 2010. The next generation of scenarios for climate change research and assessment. Nature. 463(7282): 747-756.
- Muhire, I.; and F. Ahmed. 2016. Spatiotemporal trends in mean temperatures and aridity index over Rwanda. Theoretical and Applied Climatology, 123(1-2): 399-414.
- Murphy, J. 9222. An evaluation of statistical and dynamical techniques for downscaling local climate. Journal of Climate, 19(5). 0026-0022.
- Nandintsetseg, B.; J. S. Greene, and C. E. Goulden. 2007. Trends in extreme daily precipitation and temperature near Lake Hövsgöl, Mongolia. International Journal of Climatology, 27(3): 341-347.
- Nash J. E.; and J. V. Sutcliffe. 1970. River flow forecasting through conceptual models. Part I –A discussion of principles. Journal of Hydrology 10: 282–290.
- Plattner, G. K.; T. F. Stocker. 2010. From AR4 to AR5: New Scenarios in the IPCC Process. Workshop Report.
- Piras, M.; G. Mascaro, R. Deidda, and E. Vivonia. 2016. Impacts of climate change on precipitation and discharge extremes through the use of statistical downscaling approaches in a Mediterranean basin. Total Environment. 543: 965– 980.
- Seneviratne, S. I.; M. G. Donat, B. Mueller, and L. V. Alexander. 2014. No pause in the increase of hot temperature extremes. Nature Climate Change,4(3): pp. 161-163.
- Setegn S. G. 2010. Modeling hydrological and hydrodinamic prosseses in lake Tana basin, Ethiopia. KTH. TRITA-LWR PhD Thesis 1057. Royal Institute of Technology. Sweden. Sudheer CH, Nitin ABK, Panigrahi BK and Shashi M, 2013. Streamflow forecasting by SVM with quantum behaved particle swarm optimization. Neurocomputing 101: 18–23.
- Sillmann, J. and E. Roeckner. 2008. Indices for extreme events in projections of anthropogenic climate change. Climatic Change. 86: 83-104.
- Tan, M. L., Yusop, Z., Chua, V. P., & Chan, N. W. (2017). Climate change impacts under CMIP5 RCP scenarios on water resources of the Kelantan River Basin, Malaysia. Atmospheric Research, 189, 1-10.
- Taylor, K. E.; R. J. Stouffer, and G. A. Meehl. 2012. An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93(4): 485-498.
- Vaghefi, S.A.; S. J. Mousavi, K. C. Abbaspour, R. Srinivasan, H. Yang. 2014. Analyses of the impact of climate change on water resources components, drought and wheat yield in semiarid regions: Karkheh River Basin in Iran. Hydrol. Process. 28:2018–2032.
- Van Liew M.W.; and J. Garbrecht J. 2003. Hydrologic simulation of the Little Washita River experimental watershed using SWAT. Journal of the American Water Resources Association 39:413-426.
- Willby, R. L., C. W. Dawson, and E. M. Barrow. 2002. SDSM—a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software, 17: 145-157.
- Zhang, X.; G. Hegerl, F.W. Zwiers, and J. Kenyon, 2004: Avoiding inhomogeneity in percentile-based indices of temperature extremes. J. Climate, submitted.
- Zhang, X.; and F. Yang. 2004. RClimDex (1.0) User Manual. Climate Research Branch Environment Canada Downsview, Ontario Canada
- Zhang, Q.; C. Liu, C. Xu, Y. Xu, Y, and T. Iang. 2006. Observed trends of annual maximum water level and streamflow during past 130 years in the Yangtze River basin, China. J. Hydrol. 324: 255-265.