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بررسی تغییر اقلیم بر روند دما و بارش آتی حوضه قره‌سو طبق مدل‌های CMIP6 | ||
| تحقیقات آب و خاک ایران | ||
| دوره 55، شماره 2، اردیبهشت 1403، صفحه 245-268 اصل مقاله (2.23 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2024.369146.669613 | ||
| نویسندگان | ||
| لیلی قربانی مینائی1؛ ابوالفضل مساعدی2؛ مهدی ذاکری نیا* 3؛ الهام کلبعلی4؛ محمد قبایی سوق5 | ||
| 1گروه علوم و مهندسی آب، دانشکده مهندسی آب و خاک، دانشگاه علوم کشاورزی و منایع طبیعی گرگان، گرگان، ایران. | ||
| 2گروه علوم و مهندسی آب، دانشکده کشاورزی دانشگاه فردوسی مشهد. مشهد .ایران | ||
| 3دانشیار آبیاری و زهکشی گروه مهندسی آب دانشگاه علوم کشاورزی و منابع طبیعی گرگان.شهر گرگان. ایران | ||
| 4گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه زابل، زابل، ایران | ||
| 5شرکت مدیریت منابع آب ایران، تهران، ایران | ||
| چکیده | ||
| ارزیابی وضعیت اقلیمی دوره‌های آتی با استفاده از مدل‌های اقلیمی برای در نظر گرفتن اقدامات لازم در زمینه سازگاری و یا کاهش اثرات پدیده تغییر اقلیم ضروری به‌نظر می‌رسد. در این پژوهش روند زمانی بارش، دمای حداقل و دمای حداکثر در محدوده چهار ایستگاه در زیرحوضه قره‌سو و علاوه بر آن به روش درون‌یابی تیسن در مقیاس منطقه‌ای مورد بررسی قرار گرفته است. از بین پنج مدل از مجموعه مدل‌های CMIP6، سه مدل به‌عنوان مدل برتر انتخاب و برای اجرای گروهی مدل‌ها استفاده شدند. مقیاس‌کاهی با نرم‌افزار CMHyd برای دو سناریوی SSP2-4.5 و SSP5-8.5 در سه دوره 2050-2026، 2075-2051 و 2100-2076 انجام شد. بررسی روند متغیرها در دوره پایه (2014-1990) و آتی با آزمون من‌کندال و شیب سن انجام شد. نتایج بررسی روند معنی‌داری داده‌های میانگین سالانه متغیر دمای حداکثر و حداقل تمام ایستگاه‌ها و محدوده مورد مطالعه طبق سناریوی SSP2.4-5 در دو دوره آینده نزدیک و میانه و برای سناریوی SSP5-8.5 در هر سه دوره آتی در سطح 99 درصد دارای روند معنی‌دار افزایشی است. در بررسی روند معنی‌داری داده‌های فصلی بارش طبق سناریوی SSP2.4-5 در فصل تابستان آینده دور تمام ایستگاه‌ها و آینده نزدیک ایستگاه محوطه اداره آب گرگان در سطح احتمال 95 درصد و سناریوی SSP5-8.5 فقط در فصل زمستان آینده دور ایستگاه غفارحاجی در سطح احتمال 99 درصد دارای روند معنی‌دار است. داده‌های ماهانه بارش آینده دور محدوده مورد مطالعه طبق سناریوی SSP2.4-5 در ماه Aug در سطح احتمال 99 درصد و SSP5-8.5 در ماه Mar در سطح احتمال 95 درصد دارای روند معنی‌دار است. | ||
| کلیدواژه‌ها | ||
| اجرای گروهی مدل؛ بارش؛ تغییر اقلیم؛ روند؛ مدل‌های CMIP6 | ||
| عنوان مقاله [English] | ||
| Study of future climate change on the temperature and precipitation trends in Qarasu basin based on the CMIP6 models | ||
| نویسندگان [English] | ||
| Leyli GhorbaniMinaei1؛ Abolfazl Mosaedi2؛ Mahdi Zakerinia3؛ Elham Kalbali4؛ mohammad ghabaei soogh5 | ||
| 1Department of Water Science and Engineering, Faculty of water Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran, | ||
| 2Department of Water Science and Engineering, Faculty of Agriculture, Ferdowsi University of Mashad, Mashad, Iran | ||
| 3Department of Water Science and Engineering, Faculty of water Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| 4Department of Agriculture Economy, Faculty of Agriculture, University of Zabol, Zabol, Iran | ||
| 5IRAN Water Resources Management Company, Tehran, Iran | ||
| چکیده [English] | ||
| Determining the future climate situation by using climate models seems necessary to consider in the field of adaptation or reducing the adverse effects of climate change. In this research, the temporal trend of rainfall, minimum and maximum temperature in the four stations in the Qarasu basin and in addition to, investigated using Thiessen's interpolation method. Among the five models of the CMIP6, three models were selected as the best models and used for MME. Biass Correction was done with CMHyd software for scenarios SSP2-4.5 and SSP5-8.5 in periods 2026-2050, 2051-2075 and 2076-2100. The trend of variables in the base period (1990-2014) and future were investigated with Mann-Kendall test and sens slope. The results of analysis significant trends annual average maximum and minimum temperature of all stations and in catchment area according to SSP2.4-5 scenario in two near and middle future periods and for SSP5-8.5 scenario in all three future periods have a significant trend at the 99% level. In analysis significant trend seasonal rainfall according to SSP2.4-5 scenario in the summer season distant future all stations and in near future of the station area of Gorgan regional water company at the 95% level and for the SSP5-8.5 scenario only in the winter season in the distant future Ghafarhaji station has a significant trend at the 99% level. The future monthly rainfall in the catchment area according to scenario of SSP2.4-5 in August at the 99% probability level and SSP5-8.5 in March at the 95% probability level have a significant trend. | ||
| کلیدواژه‌ها [English] | ||
| Climate Change, CMIP6 Models, Multi Model execution, Precipitation, Trend | ||
| مراجع | ||
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Almazroui, M., Ashfaq, M., Islam, M. N., Rashid, I. U., Kamil, S., Abid, M. A., ... & Sylla, M. B. (2021). Assessment of CMIP6 performance and projected temperature and precipitation changes over South America. Earth Systems and Environment, 5(2), 155-183. https://doi.org/10.1007/s41748-021-00233-6. Ashraf, B., Alizade, A., Mousavi Baygi, M., Bannayan Aval, M. (2013). Verification of Temperature and Precipitation Simulated Data by Individual and Ensemble Performance of Five AOGCM Models for North East of Iran. Journal of Water and Soil, 28(2), 253-266. https://doi.org/10.22067/JSW.V0I0.38011. (In Persian). Assamnew, A. D., and Tsidu, G. M. (2020). The performance of regional climate models driven by various general circulation models in reproducing observed rainfall over East Africa. Theoretical and Applied Climatology, 142, 1169-1189. https://doi.org/10.1007/s00704-020-03357-3. Babaeian, I., Modirian, R., Khazanedari, L., Karimian, M., Kouzegaran, S., Kouhi, M., Falamarzi, Y., & Malbusi, Sh. (2023). Projection of Iran’s precipitation in 21st Century using downscaling of selected CMIP6 Models by CMHyd. Journal of the Earth and Space Physics, 49(2), 431-449. http//doi.org/10.22059/jesphys.2023.332410.1007436. (In Persian). Bağçaci, S. Ç., Yucel, I., Duzenli, E., and Yilmaz, M. T. (2021). Intercomparison of the expected change in the temperature and the precipitation retrieved from CMIP6 and CMIP5 climate projections: A Mediterranean hot spot case, Turkey. Atmospheric Research, 256, 105576. https://doi.org/10.1016/j.atmosres.2021.105576. Carvalho, D., Rafael, S., Monteiro, A., Rodrigues, V., Lopes, M., and Rocha, A. (2022). How well have CMIP3, CMIP5 and CMIP6 future climate projections portrayed the recently observed warming. Scientific Reports, 12(1), 11983. https://doi.org/10.1038/s41598-022-16264-6. Cavazos, T., and Arriaga-Ramírez, S. (2012). Downscaled climate change scenarios for Baja California and the North American monsoon during the twenty-first century. Journal of Climate, 25(17), 5904-5915. https://doi.org/10.1175/JCLI-D-11-00425.1. Cheng, T. F., Lu, M., and Dai, L. (2019). The zonal oscillation and the driving mechanisms of the extreme western North Pacific subtropical high and its impacts on East Asian summer precipitation. Journal of Climate, 32(10), 3025-3050. https://doi.org/10.1175/JCLI-D-18-0076.1. Duan, R., Huang, G., Li, Y., Zhou, X., Ren, J., and Tian, C. (2021). Stepwise clustering future meteorological drought projection and multi-level factorial analysis under climate change: A case study of the Pearl River Basin, China. Environmental Research, 196, 110368. https://doi.org/10.1016/j.envres.2020.110368. Estoque, R. C., Ooba, M., Togawa, T., and Hijioka, Y. (2020). Projected land-use changes in the Shared Socioeconomic Pathways: Insights and implications. Ambio, 49, 1972-1981. https://doi.org/10.1007/s13280-020-01338-4. Gudmundsson, L., Boulange, J., Do, H. X., Gosling, S. N., Grillakis, M. G., Koutroulis, A. G. and Zhao, F. (2021). Globally observed trends in mean and extreme river flow attributed to climate change. Science, 371(6534), 1159-1162. https://doi.org/10.1126/science.aba3996. Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of hydrology, 377(1-2), 80-91. https://doi.org/10.1016/j.jhydrol.2009.08.003. Gupta, V., Singh, V. and Jain, M. K. (2020). Assessment of precipitation extremes in India during the 21st century under SSP1-1.9 mitigation scenarios of CMIP6 GCMs. Journal of Hydrology, 590(1), 125422. https://doi.org/10.1016/j.jhydrol.2020.125422. Haider, S., Masood, M. U., Rashid, M., Alshehri, F., Pande, C. B., Katipoğlu, O. M., & Costache, R. (2023). Simulation of the Potential Impacts of Projected Climate and Land Use Change on Runoff under CMIP6 Scenarios. Water, 15(19), 3421. https://www.mdpi.com/2073-4441/15/19/3421. Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not. Geoscientific Model Development, 15(14), 5481-5487. https://doi.org/10.5194/gmd-15-5481-2022. IPCC. (2021). Summary for policymakers Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press. Kendall, M. G. (1948). Rank correlation methods. Kofi Mensah, J., AKPOTI, K., Ofosu, E. A., Kabo-bah, A. T., Siabi, E. K., Asare, A., ... and Yidana, S. M. (2023). Evaluating Climate Change Scenarios in the White Volta Basin: A Statistical Bias-Correction Approach. Komlavi and Ofosu, Eric Antwi and Kabo-bah, Amos Tiereyangn and Siabi, Ebenezer K. and Asare, Austin and Bakuri, Ransford W. and Yidana, Sandow Mark, Evaluating Climate Change Scenarios in the White Volta Basin: A Statistical Bias-Correction Approach. https://ssrn.com/abstract=4581412 or http://dx.doi.org/10.2139/ssrn.4581412. Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the econometric society, 245-259. https://doi.org/10.2307/1907187. Massah Bavani, A., Ghasemzadeh, S., and Rozbahani, A. (2021). Predicting Climate Change Using the Multiple Group Model Approach in Qarasu Watershed. Iranian journal of Ecohydrology, 8(4), 1189-1197. https://doi.org/10.22059/IJE.2022.329152.1541. (In Persian). Meinshausen, M., Nicholls, Z. R., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., ... and Wang, R. H. (2020). The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geoscientific Model Development, 13(8), 3571-3605. https://doi.org/10.5194/gmd-13-3571-2020, 2020. Mianabadi, A., Bateni, M. M., Mohammadi, S. (2023). Projection of Change in the Distribution of Precipitation and Temperature Using Bias-Corrected Simulations of CMIP6 Climate Models (Case Study: Kerman Synoptic Station). Journal of Climate Change Research, 4(13), 65-84. https://doi.org/10.30488/CCR.2023.399780.1139. (In Persian). Modaresi, F., Araghinejad, Sh., Ebrahimi, K., and Kholghi, M. (2010). Regional Assessment of Climate Change Using Statistical Tests: Case Study of Gorganroud-Gharehsou Basin. Journal of Water and Soil, 24 (3), 476-489. https://doi.org/10.22067/JSW.V0I0.3613. (In Persian). Nash, J. E., and Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of hydrology, 10(3), 282-290. https://doi.org/10.1016/0022-1694(70)90255-6. Newton, B. W., Farjad, B., & Orwin, J. F. (2021). Spatial and temporal shifts in historic and future temperature and precipitation patterns related to snow accumulation and melt regimes in Alberta, Canada. Water, 13(8), 1013. https://doi.org/10.3390/w13081013. Niroumand fard, F., Khashei Sivaki, A., Hashemi, R., Ghorbani, Kh. (2022). Investigation of Climate Change Projection on Temperature and Precipitation Parameters Using CMIP6 Models (Case Study: Birjand Station). Iranian Journal of Soil and Water Research, 53(9), 2009-2026. http//doi.org/ 10.22059/ijswr.2022.343936.669284. (In Persian). O'Neill, B. C., Tebaldi, C., Vuuren, D. P. V., Eyring, V., Friedlingstein, P., Hurtt, G., ... and Meehl, G. A. (2016). The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9(9), 3461-3482. https://doi.org/10.5194/gmd-9-3461-2016. Rashidi Ghane, M., Motevalli, S., janbaz Ghobadi, Gh.R., Kouhi, M. (2023). Evaluation of the ability of three statistical methods to downscale the output of temperature and precipitation of CMIP6 models in the Kashfrud basin. Journal of Climate Research. 14(53), 117-132. (In Persian). Riahi, K., Van Vuuren, D. P., Kriegler, E., Edmonds, J., O’neill, B. C., Fujimori, S., ... and Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global environmental change, 42, 153-168. https://doi.org/10.1016/j.gloenvcha.2016.05.009. Roshani, A., and Hamidi, M. (2022). Forecasting the effects of climate change scenarios on temperature & precipitation based on CMIP6 models (Case study: Sari station). Journal of Water and Irrigation Management, 11(4), 781-795. 10.22059/JWIM.2022.330603.920. (In Persian). Schober, P., Boer, C., and Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia & analgesia, 126(5), 1763-1768. https://doi.org/10.1213/ANE.0000000000002864. Siabi, E. K., Awafo, E. A., Kabo-bah, A. T., Derkyi, N. S. A., Akpoti, K., Mortey, E. M., and Yazdanie, M. (2023). Assessment of Shared Socioeconomic Pathway (SSP) climate scenarios and its impacts on the Greater Accra region. Urban Climate, 49, 101432. https://doi.org/10.1016/j.uclim.2023.101432. Su, B., Huang, J., Mondal, S. K., Zhai, J., Wang, Y., Wen, S., Gao, M., Yanran, L., Jiang, S., Jiang, T., and Aiwei, L. (2021). Insight from CMIP6 SSP-RCP scenarios for future drought characteristics in china. Atmospheric Research, 250, 105375. https://doi.org/10.1016/j.atmosres.2020.105375. Tam, V. T., Batelaan, O., & Beyen, I. (2016). Impact assessment of climate change on a coastal groundwater system, Central Vietnam. Environmental Earth Sciences, 75, 1-15. https://doi.org/10.1007/s12665-016-5718-y. Thiessen, A. H. (1911). Precipitation averages for large areas. Monthly weather review, 39(7), 1082-1089. https://doi.org/10.1175/1520-0493(1911)39<1082b:PAFLA>2.0.CO;2. Wijngaard, J. B., Klein Tank, A. M. G., and Können, G. P. (2003). Homogeneity of 20th century European daily temperature and precipitation series. International Journal of Climatology: A Journal of the Royal Meteorological Society, 23(6), 679-692. https://doi.org/10.1002/joc.906. Willmott, C. J. (1981). On the validation of models. Physical geography, 2 (2), 184-194. https://doi.org/10.1080/02723646.1981.10642213. Yang, X., Zhou, B., Xu, Y., & Han, Z. (2021). CMIP6 evaluation and projection of temperature and precipitation over China. Advances in Atmospheric Sciences, 38, 817-830. https://link.springer.com/10.1007/s00376-021-0351-4. Zare Abyaneh, H., Ghabaei Sough, M., and Mosaedi, A. (2015). Drought Monitoring Based on Standardized Precipitation Evaoptranspiration Index (SPEI) Under the Effect of Climate Change. Journal of Water and Soil, 29(2), 384-392. https://doi.org/10.22067/JSW.V0I0.36472. (In Persian). Zarei, A., Mousavi, S. F., Gordji, M. E., and Karami, H. (2019). Optimal reservoir operation using bat and particle swarm algorithm and game theory based on optimal water allocation among consumers. Water Resources Management, 33(9), 3071-3093. https://doi.org/10.1007/s11269-019-02286-9. Zarrin, A., and Dadashi-Roudbari, A. (2022). Technical Note: Assessing the Effect of Climate Change on Heavy Precipitation in Iran Based on a CMIP6 Ensemble Model. Journal of Water and Sustainable Development, 8(4), 119-124. 20.1001.1.24235474.1400.8.4.14.9. (In Persian). | ||
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