| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 2,846 |
| تعداد مقالات | 32,300 |
| تعداد مشاهده مقاله | 41,387,078 |
| تعداد دریافت فایل اصل مقاله | 18,562,166 |
ارزیابی کارایی مدل گردش عمومی CanESM2 و مدل منطقه ای REMO به منظور پیش بینی تغییرات ویژگی های اقلیمی در آبخیز جازموریان | ||
| پژوهش های آبخیزداری | ||
| مقاله 6، دوره 33، شماره 4 - شماره پیاپی 129، دی 1399، صفحه 61-76 اصل مقاله (1.37 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmej.2020.341365.1300 | ||
| نویسندگان | ||
| زهره ابراهیمی خوسفی* 1؛ مریم میراکبری2 | ||
| 1استادیار گروه مهندسی طبیعت، دانشکده منابع طبیعی، دانشگاه جیرفت | ||
| 2دکترای تخصصی بیابانزدایی، دانشکده منابع طبیعی دانشگاه تهران | ||
| چکیده | ||
| گرمشدن سیارهی زمین بهدلیل افزایش تغییر در بارندگی و دما بر دست‌رسی به منابع آب، بهویژه در منطقه‌های خشک، اثری جدی دارد. مدلهای اقلیمی گردش عمومی جو (GCM) و منطقهیی (RCM) ابزار اصلی ارزیابی تغییر ویژگیهای اقلیمی در آینده است. متفاوتبودن تفکیک مکانی این مدلها سبب اختلاف در نتیجه‌های ارزیابی تغییر ویژگیهای اقلیمی میشود. ازاینرو در این پژوهش کارآیی مدل بزرگ مقیاس CanESM2 و مدل منطقهیی REMO در آبخیز جازموریان با کاربرد معیارهای آماری ارزیابی شد. نتیجه‌ها نشان داد که مدل CanESM2 عمل‌کرد بهتری از مدل منطقهیی REMO برای پیشبینی ویژگیهای اقلیمی دارد. شبیهسازی ویژگیهای اقلیمی برپایه‌ی مدل CanESM2 نشان داد که بارندگی در حالت‌های ممکن RCP2.6، RCP4.5 و RCP8.5 به ترتیب 19/23، 18/55 و 14/55 میلیمتر در ایستگاه ایرانشهر و در ایستگاه بم 8/31، 10/66 و 15/72 میلیمتر نسبت به دورهی مشاهده کاهش خواهد یافت. میانگین مقادیر شبیهسازی شده‌ی دما در حالت‌های ممکن پژوهش نشان داد که دمای متوسط در ایستگاه ایرانشهر به ترتیب 1/57، 2/15 و 3/1 درجهی سانتیگراد و در ایستگاه بم 1/84، 2/31 و 3/35 درجهی سانتیگراد نسبت به دورهی مشاهده افزایش خواهد یافت. به طورکلی نتیجه‌های پیشبینی ویژگیهای بارندگی و دمای متوسط در آبخیز جازموریان نشان داد که احتمال وقوع دورههای خشک طولانیتر در آینده نسبت به دورهی مشاهده در این منطقه افزایش خواهد یافت. ازاینرو اطلاع از روند تغییر ویژگیهای اقلیمی میتواند به مدیران و برنامهریزان در دادن راه‌کارهای لازم برای شرایط تغییر اقلیم آینده کمک کند. | ||
| کلیدواژه‌ها | ||
| جازموریان؛ روند تغییرات؛ من- کندال؛ مدل بزرگ مقیاس؛ مدل منطقه ای | ||
| عنوان مقاله [English] | ||
| Performance Evaluation of the CanESM2 Global Circulation Model and the REMO Regional Model to Predict Changes of Climatic Parameters in the Jazmourian Watershed | ||
| نویسندگان [English] | ||
| Zohre Ebrahimi Khusfi1؛ Maryam Mirakbari2 | ||
| 1Assistant Professor, Department of Natural Science, Faculty of Natural Resources, University of Jiroft, Jiroft, Iran | ||
| 2PhD, Faculty of Natural Resources, University of Tehran, Iran | ||
| چکیده [English] | ||
| Global warming has a serious impact on access to water resources, especially in arid regions due to changes in rainfall and temperature. The Global Circulation Models (GCM) and the Regional Climate Models (RCM) have been considered as the main tools for assessing changes in climatic variables in the future. The difference in the spatial resolution of these models causes the different results in climate change assessment. The performance of the CanESM2 and REMO models was evaluated using statistical criteria in the Jazmourian Watershed. The results indicated that the CanESM2 has performed better than the REMO regional model for predicting climatic parameters. Climate parameter simulation based on the CanESM2 model showed that precipitation will decrease under the RCP scenarios (RCP2., RCP4.5, RCP8.5) by 19.23, 18.55, 14.55 mm, respectively at the Iranshahr Station and 8.31, 10.6, 15.72 mm, respectively at the Bam Station. The mean projected temperature based on the RCP scenarios showed that temperature will increase by 1.57, 2.15 and 3.1 ºC at the Iranshahr Station and 1.84, 2.31 and 3.35 ºC at Bam station under RCP2.6, RCP4.5 and RCP8.5, respectively. Generally, the finding of simulated precipitation and temperature in the Jazmourian Watershed showed that the long dry periods is more likely to occur in the future as compared to the historical period. Hence, knowing the trend of the changes in climatic variables can help managers and planners to provide required strategies under the future climate change conditions. | ||
| کلیدواژه‌ها [English] | ||
| Changes Trend, Jazmourian, Large-scale model, Mann- Kendall, Regional Model | ||
| مراجع | ||
|
Aghakhani Afshar A, Hassanzadeh Y, Besalatpour A, Pourreza-Bilondi M. 2016. Annual assessment of Kashafrood Watershed Basin climate components in future periods using fifth report of intergovernmental panel on climate change. Water and Soil Conversation. 23(6): 217– 233. (In Persian). Almazroui M, Nazrul Islam M, Saeed F, Alkhalaf AK, Dambul R. 2017. Assessing the robustness and uncertainties of projected changes in temperature and precipitation in Ar5 global climate models over the Arabian Peninsula. Atmospheric Research. 194: 202–13. Bazrkar MH, Zamani N, Eslamian S, Eslamian A, Dehghan D. 2015. Urbanization and climate change. Handbook of Climate Change Adaptation, 619–655, Springer, Berlin. Barkhori S, Mahdavi R, Zehtabian Gh, Gholami H. 2019. Evaluation of the climatic variables of Jiroft Plain using HadCM3 model in future periods. Journal of Range and Watershed Management. 71 (2): 355– 366. (In Persian). Djalante R. 2019. Key assessments from the IPCC special report on global warming of 1.5 °C and the implications for the Sendai framework for disaster risk reduction. Progress in Disaster Science, 1: 100001. Ebrahimi Khusfi Z, Roustaei F, Ebrahimi Khusfi M, Naghavi S. 2020 A. Investigation of the relationship between dust storm index, climatic parameters, and normalized difference vegetation index using the ridge regression method in arid regions of Central Iran.Arid Land Research and Management. p. 1–25. Ebrahimi Khusfi Z, Khosroshahi M, Roustaei F, Mirakbari M. 2020 B. Spatial and seasonal variations of sand-dust events and their relation to atmospheric conditions and vegetation cover in semi-arid regions of central Iran.Geoderma,365: 114–225. Gebrechorkos SH, Hulsmann S, Bernhofer Ch. 2019. Statistically downscaled climate dataset for East Africa. Scientific Data. 6 (1): 1–8. Gholami A, Shahedi K, Habibnejad Roushan M, Vafakhah M, Soleimani K. 2017. Projection and comparison of future climate changes using global circulation models under different scenarios in Talar Watershed Basin of Mazandaran. Rang and Watershed Management. 70(1): 181– 196. (In Persian). Gebremeskel S, Liu YB, de Smedt F, Hoffmann L, Pfister L. 2005. Analysing the effect of climate changes on stream flow using statistically downscaled GCM scenarios. International Journal River Basin Management. 2(4): 271–280. Green TR, Taniguchi M, Kooi H, Gurdak JJ, Allen DM, Hiscock KM, Treidel H, Aureli A. 2011. Beneath the surface of global change: Impacts of climate change on groundwater. Journal of Hydrology. 405(3): 532–560. IPCC. 2014. Summary for policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. Cambridge. United Kingdom and New York. USA. p. 1–32. Jahanbakhsh Asl S, Khorshiddoust A, Alinejad MH, Pourasghar F. 2016. Impact of climate change on projection and temperature by taking the uncertainty of models and climate scenarios (Case study: Shahrchay basin in Urmia). Hydrogeomorphology. 2(7): 107–122. (In Persian). Jacob D, Elizalde A, Haensler A, Hagemann S, Kumar P, Podzun R, Rechid D, Remedio AR, Saeed F, Sieck K. 2012. Assessing the transferability of the regional climate model Remo to different Coordinated Regional Climate Downscaling Experiment (Cordex) Regions. Atmosphere. 3(1): 181–99. Kalanaki M, Karandish F. 2015. Predicting the long- term effect of climate change on climatic variables in humid region. Irrigation and Water Engineering. 5(20): 131–148. (In Persian). Kendall M. 1975. RankCorrelation Methods (4th Edn.) Charles Griffin. San Francisco, CA, 8. Lee S, Yoo SH, Choi JY, Bae S. 2017. Assessment of the impact of climate change on drought characteristics in the Hwanghae Plain, North Korea using time series SPI and SPEI: 1981–2100. Water. 9 (8): 579. Mann HB. 1945. Nonparametric Tests against Trend. Econometrica. Journal of the Econometric Society. 245–59. Mesbahzadeh T, Miglietta .M, Mirakbari M, Soleimani Sardoo F, Abdolhoseini M. 2019. Joint modeling of precipitation and temperature using copula theory for current and future prediction under climate change scenarios in arid lands (Case study: Kerman Province, Iran). Advances in Meteorology. 2019: 1–15. Mirgol B, Nazari M. 2018. Possible Scenarios of Winter Wheat Yield Reduction of Dryland Qazvin Province, Iran, Based on Prediction of Temperature and Precipitation Till the End of the Century. Climate. 6 (4): 78. Mirakbari M, Mesbahzadeh T, Mohseni Saravi M, Khosravi H, Mortezaie Gh. 2018. Performance evaluation of CMIP5 series model in projection and simulation of climatic variables of precipitation, temperature and wind speed, case study: Yazd Province. Natural Geography Research. 50 (3): 593–609. (In Persian). Modarres R, Ghadami M, Naderi S, Naderi M. 2018. Future extreme rainfall change projections in the north of Iran. Meteorological Applications. 25 (1): 40–48. (In Persian). Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, Van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T. 2010. The Next Generation of Scenarios for Climate Change Research and Assessment. Nature. 463 (7282): 747–756. Rezaei M, Ghasemieh H. 2019. Assessing the impact of climate change on rainfall and temperature variability (Case study: Kashan and Khur and Biabank Stations). Arid Biome Scientific Journal. 9 (1): 8– 98. (In Persian). Rui-li L, Geng Sh. 2013. Impacts of Climate Change on Agriculture and Adaptive Strategies in China. Journal of Integrative Agriculture. 12(8): 1402–08. Salimi A. 2020. Assessment of wind erosion changes based on regional climate change model, case study: Isfahan province. Ms.C thesis, University of Tehran, Iran. 160 p. (In Persian). Tabari H, Marofi S, Aeini A, Hosseinzadeh Talaeea P, Mohammadi K. 2011. Trend analysis of reference evapotranspiration in the western half of Iran. Agricultural and Forest Meteorology. 151(2):128–136. Tang J, Niu X, Wang Sh, Gao H, Wang X, Wu J. 2019. Statistical downscaling and dynamical downscaling of regional climate in China: Present climate evaluations and future climate projections. Journal of Geophysical Research: Atmospheres. 121 (5): 2110– 2129. Ujeneza EL. 2014 Simulation the Characteristics of Droughts in Southern Africa. Ms.c thesis, University of Cape Town. Von Storch H. 1999. Misuses of statistical analysis in climate research. In: Analysis of climate variability, 1–26, Springe, Berlin, Heidelberg. Vaghefi SA, Keykhai M, Jahanbakhshi F, Sheikholeslami J, Ahmadi A, Yang H, Abbaspour K. 2019. The future of extreme climate in Iran. Nature. 9(1): 1–11. Wang XL, Feng Y, Compo GP, Swail VR, Zwiers FW, Allan RJ, Sardeshmukh PD. 2013. Trends and Low Frequency Variability of Extra-Tropical Cyclone Activity in the Ensemble of Twentieth Century Reanalysis. Climate Dynamics. 40(11–12): 2775–2800. Wang J, Hu L, Li D, Ren M. 2020. Potential Impacts of Projected Climate Change under CMIP5 RCP Scenarios on Streamflow in the Wabash River Basin. Advances in Meteorology. 10 Feb 2020: 1– 18. Ye L, Shi K, Zhang H, Xin Zh, Hu J, Zhang Ch. 2019. Spatio-temporal analysis of drought indicated by SPEI over northeastern china. Water. 11(5): 2–18. | ||
|
آمار تعداد مشاهده مقاله: 748 تعداد دریافت فایل اصل مقاله: 852 |
||