بررسی همبستگی بارش‌های پاییزه حوضه‌های آبریز ایران با نمایه‌های دورپیوندی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آبیاری و آبادانی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

2 گروه اقلیم‌شناسی، دانشکده جغرافیا، دانشگاه خوارزمی، تهران، ایران

3 شرکت مدیریت منابع آب ایران، تهران، ایران

4 پژوهشکده هواشناسی و علوم جو، تهران، ایران

5 گروه مهندسی آب، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران

چکیده

تغییرات بارش فصلی و سالانه تابع عوامل طبیعی و اقلیمی متعددی است که یکی از مهم­ترین آن­ها نمایه­های دورپیوندی هستند. هدف مطالعه کنونی، ارزیابی و بررسی همبستگی نمایه­های دورپیوندی با بارش­های پاییزه حوضه­های آبریز 30 گانه ایران است. به این منظور، داده­های بارش فصل پاییز 717 ایستگاه سینوپتیک، اقلیم­شناسی و باران­سنجی در یک دوره اقلیمی 28 ساله (1988-2015) بدون خلأ آماری و همگن انتخاب و همبستگی آن­ها با هشت نمایه دورپیوندی SOI، MEI، NAO، AO، NCP، C-SST، M-SST و P-SST با هشت تاخیر زمانی به دست آمد، سپس معناداری آن­ها بررسی و در نهایت تحلیل شدند. نتایج نشانگر همبستگی مثبت معنادار نمایه­های MEI (1/29 تا 3/43 درصد ایستگاه­های حوضه­های غربی، جنوبی، شرقی و شمال شرقی) و نمایه NAO (4/29 درصد ایستگاه­های حوضه­های شمال غربی) بودند در حالی که دو نمایه SOI (در 3/23 تا 7/53 ایستگاه­های حوضه­های غربی، جنوبی، شرقی و شمال شرقی) و C-SST (3/23 ایستگاه­های حوضه­های جنوب شرقی) دارای همبستگی منفی و معنادار بودند. از نظر گام زمانی مشخص شد شاخص­های Oct-MEI (3/43 درصد ایستگاه­ها همبستگی مثبت) و Aug-SOI (7/53 درصد ایستگاه­ها همبستگی منفی) با بیشترین ایستگاه­های مورد مطالعه همبستگی معنادار داشتند. بررسی حوضه­ای مشخص کرد فراوانی همبستگی­های معنادار در حوضه­های آبریز مختلف بین 9/10 الی 36 درصد متغیر است. نتایج این پژوهش نشان می­دهد که حدود 10 حوضه از حوضه­های مورد مطالعه با بیش از 30 درصد نمایه­های دورپیوندی همبستگی معنادار دارند که در هر حوضه آبریز متغیر است. بنابراین، نمایه­های دورپیوندی را با گام­های زمانی مختلف می­توان به عنوان متغیرهای پیش­بینی کننده بارش پاییزه در حوضه­های آبریز ایران مورد استفاده قرار داد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Investigating the relationship between climate Teleconnection Indices and Autumnal Rainfall in Iran Watersheds

نویسندگان [English]

  • Jalil Helali 1
  • Elham Pishdad 2
  • Masoumeh Alidadi 2
  • Sedigheh Loukzadeh 3
  • Ebrahim Asadi Oskouei 4
  • Reza Norooz Valashedi 5
1 Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
2 Department of Climatology, Faculty of Geography, Kharazmi University, Tehran, Iran
3 Iran Water Resources Management, Tehran, Iran
4 Atmospheric Science & Meteorogical Research Center, Tehran, Iran
5 Assistant Professor, Water Engineering Department, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
چکیده [English]

Seasonal and annual precipitation variations are subject to numerous natural and climatic factors such as climate teleconnection indices (CTIs). The purpose of this study is to investigate the correlation between CTIs and autumnal precipitation in 30 catchments of Iran. For this purpose, autumnal precipitation data were selected from 717 synoptic, climatological and rain gauge stations over a 28-year (1988–2015) climate period, and their correlation with eight CTIs including SOI, MEI, NAO, AO, NCP, C-SST, M-SST, and P-SST were obtained in eight-time lags. Finally, their correlation significance were investigated and analyzed. The results showed a significant positive correlation between the MEI (29.1% to 43.3% of the western, southern, eastern and northeast watershed stations) and the NAO (29.4% of the northwest basin stations), While the SOI (23.3-53.7 stations of the western, southern, eastern and northeast watersheds) and C-SST (23.3 of the southeast watershed stations) had significant negative correlation. In terms of the time step, it was found that the Oct-MEI (43.3% of the stations had a positive correlation) and Aug-SOI (53.7% of the stations had a negative correlation) had a significant correlation with most of the studied stations. From the watershed point of view, it was found that the frequency of significant correlations in different catchments varied between 10.9 and 36%. The results of this study show that about 10 watersheds have a significant correlation with more than 30% of CTIs which changes in each watershed. Consequently, the CTIs with different time steps can be used as predictor variables for autumn precipitation in Iran watersheds.

کلیدواژه‌ها [English]

  • Climate Teleconnection Index
  • Autumnal Precipitation
  • Iran Watersheds
  • Sea Surface Temperature
Ahmadi, M., Fathniya A., Abkharabat. S., 2015. Trend Analysis of Iran's Precipitation and Its Relation to the Teleconnection Forces, Journal of Climate Research, 23: 19-33.
Ahmadi, M., Salimi, S., Hosseini, S.A., Poorantiyosh, P., Bayat, A. 2019, Iran's precipitation analysis using synoptic modeling of major teleconnection forces (MTF), Dynamics of Atmospheres and Oceans 85: 41–56.
Amirmoradi, K., Sabziparvar, A.A., Deihimi, A., 2015. Analysis of the Relationship between Seasonal Streamflow Variations and some Teleconnection Indices by Wavelet Analysis Method (Case study: Northwest Rivers), Water and Soil Science Journal, 4(1):269-284.
Araghi, A., Martinez, C.J., Adamowski, J., Olesen, J.E., 2019. Associations between large-scale climate oscillations and land surface phenology in Iran, Agricultural and Forest Meteorology 278:107682.
Davey, M.K., Brookshaw, A., Ineson, S., 2014. The probability of the impact of ENSO on precipitation and near-surface temperature, Climate Risk Management 1: 5–24.
Dezfuli, A.K., Karamouz, M. and Araghinejad, S. 2010. On the relationship of regional meteorological drought with SOI and NAO over southwest Iran. Theoretical and Applied Climatology, 100(1):57–66. DOI: 10.1007/s00704-009-0157-2
Fatemi, M., Omidvar, K., Mazidi,A., Mesgari, E., Dehghan, H., 2017, Spatial analysis and study of Tele-connection patterns of drought in central Iran, Arid Biome Scientific and Research Journal, 7(1):51-65.
Gelcer, E., Fraisse, C.W., Zotarelli, L., Stevens, F.R., Perondi, D., Barreto, D.D., Malia, H.A., Ecole, C.C., Montone, V., Southworth, J., 2018. Influence of El Niño-Southern oscillation (ENSO) on agroclimatic zoning for tomato in Mozambique, Agricultural and Forest Meteorology 248: 316–328.
Ghaedamini, H., Nazem Alsadat, S.M.J., Kouhizadeh, M., Sabziparvar, A.A., 2014. Individual and coupled effects of the ENSO and PDO on autumnal dry and wet periods in the southern parts of Iran, Iranian Journal of Geophysics, 8(2):92-109.
Ghasemi, A.R. and Khalili, D., 2008. The Effect of the North Sea Caspian Pattern (NCP) on winter temprature in Iran, Theoretical and Applied Climatology, 92(1): 59-74. DOI: 10.1007/s00704-007-0309-1
Ghavidel Rahimi, Y., Farajzadeh Asl, M., Kakapor, S., 2014. Investigation on North Sea-Caspian Teleconnection Pattern Effect on Autumn Rainfall Fluctuations in West and Northwest Regions of Iran, Planning and Geography 49:217-230.
Ghayour, H. Khosravi, M, 2000, ENSO effect on summer and Autumnal Precipitations Anomaly in Southeast of Iran, Geography Researches, 62:141-174.
Ghayoor, H. A. and Asakereh, H. 2002, Effects of Teleconnection on Climate of Iran, Case Study: The North Atlantic Oscillation and the Southern Oscillation effects on changes in mean monthly temperature of Jask, Journal of Geographical Research, 25:16-17.
Ghodousi, H., Kooshafar, L., 2018. Simultaneous Use of Climatic Signals and Sea Surface Temperature for Flow Forecasting (Case study: Cheshmeh Kileh catchment area), Iranian Journal of Soil and Water Research, 49:1043-1053.
Golchin Kohi, R., 2004. The influence of pressure oscillations in North Atlantic on precipitation of Iran, MSc Thesis on Irrigation, Shiraz University.
Gong, D.Y. and Wang, S.W., 1999. Impacts of ENSO on global precipitation changes and precipitation in China. Chinese Science Bulletin 44(9): 852-857. DOI: 10.1007/BF02885036
Goudarzi, M., Ahmadi, H., Hosseini, S.A., 2017. Examination of relationship between teleconnection indexes on temperature and precipitation components (Case Study: Karaj Synoptic Stations), Eco hydrology, 4(3):641-651.
Helali, J. 2018. Seasonal prediction of rainfed wheat yield by crop models and statistical methods, PhD thesis In Agrometeorology, University of Tehran, Karaj, Iran.
Jiang, R., Wang, Y., Xie, J., Zhao, Y., Li, F., Wang, X., 2019, Assessment of extreme precipitation events and their teleconnections to El Niño Southern Oscillation, a case study in the Wei River Basin of China, Atmospheric Research 218:372–384.
Khorshid Doust A.M., Ghavidel Rahimi, Y., 2006. Evaluation of ENSO on Seasonal Precipitaion Variability at East Azerbaijan Province by MEI, Geography Researches, 57: 15-26.
Kianipour, M., 2000. Synoptic investigation of El Nino and Its relation on south and Southwest Precipitation anomalies, MSc Thesis of Climatology, Jan 2000, Tarbiat Modares University.
Kutiel, H. and Turkes, M., 2005. New Evidence for the role of the North Sea Caspian Pattern on the temperature and precipitation regimes in continental central Turkey, Geografiska Annaler: Series A, Physical Geography, 87(4): 501–513. DOI: 10.1111/j.0435-3676.2005.00274.x
Lee J.H., Julien P.Y., 2016.Teleconnections of the ENSO and South Korean precipitation patterns, Journal of Hydrology 534: 237–250.
Lee, J.H., Lee, J., Julien, P.Y., 2018, Global climate teleconnection with rainfall erosivity in South Korea, Catena 167: 28–43.
Liesch, T., Wunsch, A., 2019. Aquifer responses to long-term climatic periodicities, Journal of Hydrology 572: 226–242.
Modarresi, F., Araghinejad, S., Ebrahimi, K., 2015. The Combined Effect of Seasonal Fluctuations of Persian Gulf and Mediterranean Sea Surface Temperature on Monthly Streamflow Forecasting of Karkheh River, Iran, Iranian Journal of Soil and Water Research, 46:597-607.
Mohammadi, K., Goudarzi, N., 2018. Study of inter-correlations of solar radiation, wind speed and precipitation under the influence of El Nino Southern Oscillation (ENSO) in California, Renewable Energy 120:190-200.
Mostafavi Darani, S. M., Khoshhal, J., Stone, R., Abbasi, F., Babaeian, I., 2014. An approach to yield prediction of barley using teleconnection signals (Case study: Kabootarabad, Isfahan), Journal of Agricultural Meteorology, Vol. 2, No. 2: 24-36.
Mousavi Baygi, M., Fallah Ghalhari, G.A., Habibi, M., Nokhandan, M., 2008. Assessment of the relation between the large scale climatic signals with rainfall in the Khorassan, J. Agric. Sci. Natur. Resour., Vol. 15(2):217-224.
Nalley, D., Adamowski, J., Biswas, A., Gharabaghi, B., Hu, W., 2019, A multiscale and multivariate analysis of precipitation and streamflow variability in relation to ENSO, NAO and PDO, Journal of Hydrology 574: 288–307.
Nazemosadat, M.J. and Cordery, I., 2000. On the relationships between ENSO and autumn rainfall in Iran, International Journal of Climatology, 20 (1): 47–61. DOI: 10.1002/ (SICI) 1097-0088(200001)20:13.0.CO; 2-P
Nazemosadat, M.J., Samani, N., Barry, D.A. and Molaii Niko, M., 2006. ENSO Forecasting on climate change: precipitation analysis, Iranian Journal of Science & Technology, Transaction B, Engineering, 30 (B4): 555-565.
Paydar Ardakani, A., 2001. The impact of North Atlantic Oscillation on both precipitation and temperature fluctuations in Iran, MSc thesis on desert management, Shiraz University.
Sadatinejad, S.J., Shekari, M.R., Vali, A., 2016. Forecasting of Monthly rainfall using teleconnection climate indices using of artificial neural network and statical models (Case study: Sheshde and gharebolagh adjacent stations), Ecohydrology, 3(3):391-403.
Sobral, B.S, de Oliveira-Júnior, J.F., de Gois, G., Pereira-Júnior, E.R., de Bodas Terassi, P.M., Muniz-Júnior, J.G.R., Lyra, G.B, Zeri, M., 2019. Drought characterization for the state of Rio de Janeiro based on the annual SPI index: trends, statistical tests and its relation with ENSO, Atmospheric Research 220:141–154.
Sun, X., Renard, B., Thyer, M., Westra, S., Lang, M., 2015, A global analysis of the asymmetric effect of ENSO on extreme precipitation, Journal of Hydrology 530:51–65.
Yarahmadi,D. & Azizi, G., 2007, Muti vaiable analysis of relation of seasonal Precipitation and Climatic Index, Geography Researches, 62:161-174.