امکان‌سنجی استفاده از شاخص‌های پیوند ازدور در پیش‌بینی بارش فصل بهار حوضه‌های آبریز ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

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

2 گروه جغرافیای طبیعی، دانشکده علوم زمین، دانشگاه شهید بهشتی، تهران، ایران

3 گروه مهندسی آبیاری و آبادانی، دانشکده فناوری و مهندسی کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

4 استادیار گروه علوم و مهندسی آب، دانشگاه اراک، اراک، ایران.

چکیده

مدیریت و برنامه‌ریزی در زمینه منابع آب در فصول مختلف به خصوص فصل بهار به منظور بهره‌برداری در بخش‌های کشاورزی، صنعتی و شرب در مناطق خشک و نیمه خشک جهان به خصوص ایران بسیار حیاتی است. شاخص‌های پیوندازدور به عنوان شاخص‌های بزرگ‌مقیاس می‌توانند در رفتار هیدرولوژیکی در سطح حوضه آبریز موثر باشند. در این مطالعه سعی شد ارتباط بین این شاخص‌ها و بارش‌های فصل بهار در حوضه‌های آبریز ایران مورد بررسی قرار گرفته و امکان استفاده از آن­ها به عنوان متغیرهای پیش­بینی کننده مورد ارزیابی قرار گیرد. به این منظور همبستگی 40 شاخص پیوندازدور با فرکانس­های مختلف در تاخیرهای زمانی 6 تا 1 ماهه با بارش فصل بهار مورد بررسی قرار گرفت. نتایج نشان داد درصد ایستگاه­هایی که همبستگی بارش فصل بهار آن­ها با شاخص­های پیوندازدور معنادار است بسته به حوضه آبریز متغیر است. به طور کلی، شاخص‌های مرتبط با ENSO و SST بیشترین فراوانی همبستگی‌های معنادار با بارش بهار در نیمه شمالی، شمال غربی، شمال شرقی و گاهی جنوب غربی را دارند. این شاخص­ها در حوضه دریای خزر در تأخیر 3 تا 6 ماهه، خلیج فارس-دریای عمان 1 تا 3 ماهه، دریاچه ارومیه 3 ماهه، فلات مرکزی 1 تا 4 ماهه، مرز شرقی 1 و 6 ماهه، و حوزه قره‌قوم تأخیر 1 و 3 ماهه بیشترین مقدار را دارند. به طور کلی، می‌توان گفت بارش فصل بهار در بسیاری از ایستگاه‌های واقع در حوضه‌های آبریز نیمه شمالی ایران با شاخص‌های پیوندازدور همبستگی معنادار دارند ولی در بخش جنوبی، فلات مرکزی و بخش شرقی کمترین همبستگی را دارند که این امر به دلیل بارش کم در مناطق جنوبی در این فصل است. بررسی نتایج نشان داد کارایی مدل MLP نسبت به مدل MLR در مدلسازی مقدار بارش فصل بهار عمده حوضه­های آبریز بالاتر است.

کلیدواژه‌ها


عنوان مقاله [English]

Feasibility study of using Climate Teleconnection Indices in prediction of spring precipitation in Iran Basins

نویسندگان [English]

  • Jalil Helali 1
  • Touran Hosseinzahef 2
  • Majid Cheraghalizadeh 3
  • Mehdi Mohammadi Ghalenei 4
1 Department of Irrigation and Reclamation Engineering Department, Faculty of College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran.
2 Department of Natural Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
3 Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
4 Department of Water Sciences & Engineering, Faculty of Agriculture and Engineering, Arak University, Arak, Iran
چکیده [English]

Management and planning in the field of water resources in different seasons, especially in spring, is very vital for exploitation in the agricultural, industrial and drinking sectors in arid and semi-arid regions of the world, especially Iran. Climate Teleconnection Indices (CTI) as large-scale indices can be important in hydrological behavior at the basin scale. In this study, the relationship between these indices and spring rainfall in the basins of Iran was investigated and the possibility of using them as predictor variables was identified. For this purpose, the correlation of 40 CTI in time delays of 6 to 1 month with spring rainfall was investigated. The results showed that the percentage of stations that have a significant correlation with spring precipitation, varies depending on the location of the basin, but in general, the indicators related to ENSO and SSTs have the most frequent significant correlations with spring rainfall in the northern half, northwest, northeast and sometimes southwest springs. These indicators in the Caspian Sea basin with a delay of 3 to 6 months, Persian Gulf-Oman Sea with a delay of 1 to 3 months, Lake Urmia with a delay of 3 months, Central Plateau with a delay of 1 to 4 months, Eastern border with a delay of 1 and 6 months, and Qarahqom basin with a delay of 1 and 3 months have the highest amount. In general, it can be said that the spring rainfall in many stations located in the watersheds of the northern half of Iran have a significant correlation with the CTI, but in the southern, the central plateau and the eastern part have the lowest correlation, which is due to the low rainfall in this season in the southern regions. The results showed that the efficiency of predicting spring precipitation by MLP model is better than MLR model.

کلیدواژه‌ها [English]

  • Climate Teleconnection Indices
  • Basin
  • spring precipitation
  • Multi-Layer Perceptron
  • multi linear regression
Ahmadi, M. Salimi, S., Hosseini, S.A., Poorantiyosh, H.A. and Bayat, A., )2019(. Iran's precipitation analysis using synoptic modeling of major teleconnection forces (MTF), Dynamics of Atmospheres and Oceans 85:41–56.DOI:10.1016/j.dynatmoce.2018.12.001
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.DOI: 10.1016/j.agrformet.2019.107682
Behar, O., Khellaf, A. and Mohammedi, K., )2015(. Comparison of solar radiation models and their validation under Algerian climate–the case of direct irradiance. Energy Convers. Manage. 98:236-251.DOI: 10.1016/j.enconman.2015.03.067
Canchala, T., Alfonso-Morales, W., Carvajal-Escobar, Y., Cerón, W.L. and Caicedo-Bravo, E., )2020(. Monthly Rainfall Anomalies Forecasting for Southwestern Colombia Using Artificial Neural Networks Approaches, Water, 12, 2628.DOI:10.3390/w12092628
Choubin, B., Khalighi Sigarooodi, S., Malekian, A., Ahmad, S. and Attarod, P.M, )2014(. Drought Forecasting in a Semi-arid Watershed Using Climate Signals: a Neuro-fuzzy Modeling Approach, J. Mt. Sci. 11(6):1593-1605.DOI: 10.1007/s11629-014-3020-6
Dargahian, F., Doostkamian, M. and Taheriyan, A., )2019(. Analysis of synoptic -dynamic weather Changes spring precipitation Comprehensive Iran, Desert Ecosystem Engineering Journal, 8(24):19-36. DOI:10.22052/deej.2018.7.24.11
Dargahian F., Doostkamian M. and Taherian M., )2020(. Statistical and synoptic analysis of temperature advection role in Iran spring rainfalls (1961-2013), Environ. Water Eng., 5(4), 276–291. DOI:10.22034/jewe.2019.198398.1329.
Doblas-Reyes, F.J., Garcia-Serrano, J., Lienert, F., Biescas, A.P. and Rodrigues, L.R.L., )2013(. Seasonal climate predictability and forecasting: status and prospects. WIREs Clim. Chan. 4:245-268. DOI:10.1002/wcc.217.
Fallah Ghalhary, G., )2012(. The assessment of the role of climatic signal changes on spring rainfall oscillations, Case study: Khorasan Razavi Province, Journal of the Earth and Space Physics, 37(3):155-171.
Fatehi Marj, A. and M.H.Mahdian, )2009(. Autumn rainfall forecasting using ENSO indices by Neural Network method, Watershed Management Researches (Pajouhesh & Sazandegi), 84: 42-52.
Fatehi Marj, A., Borhani Darian, A. and Mahdian, M.H., )2006(. Streamflow forecasting using climatic signals in the Urmia Lake Basin, Pajouhesh & Sazandegi, 71:41-51.
Fatehi Marj, A., Tajaddini, M. and Salajagheh, A., )2015(. Study of the relationship between the some climate signals (SOI, NAO, MEI, NINO) and meteorological drought in Kerman province, Iran, Journal of Agricultural Meteorology, 3 (1):25-39.
Fatemi, M., Omidvar, K., Mazidi, A., Mesgari, E. and 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.DOI:10.29252/ARIDBIOM.7.1.51
Ghaedamini, H., Nazemosadat, S.M.J., Kouhizadeh, M.and 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.
Ghavidel Rahimi, Y., )2004(. Due to the large scale atmospheric circulation patterns-Ocean on ENSO variability in seasonal climatic effects of El Nino and La Nina spring rainfall variability in East Azarbaijan, Journal of Human Sciences Lecturer, Geography Supplement,35:44-49.
Haghighi, E., Gholizadeh, M.H.,Doostkamian, M. and Ghaderi, F., )2017(. Nature and structure of the atmospheric circulation in pervasive rains of spring, Iran, Physical Geography Research Quartery, 49(3):523-539. DOI: 10.22059/JPHGR.2017.218909.1006955
Hejazizadeh, Z., Fatahi, E., Saligheh, M. and Arsalani, F., )2013(. Study on the impact of climate signals on the precipitation of the central of Iran using ANN, Applied researches on Geographical Sciences, 29: 75-89.
Helali, J., Salimi, S., Lotfi, M., Hosseini, S.A., Bayat, A., Ahmadi, M. and Naderizarneh, S., )2020a(. Investigation of the effect of large-scale atmospheric signals at different time lags on the autumn precipitation of Iran’s watersheds, Arabian Journal of Geoscience, 13(18):1-24.DOI: 10.1007/s12517-020-05840-7
Helali, J., Pishdad, E., Alidadi, M., Loukzadeh, S., Asaadi Oskouei, E. and Norooz R., )2020b(. Investigating the relationship between climate Teleconnection Indices and Autumnal Rainfall in Iran Watersheds, Iranian Journal of Soil and Water Research, 51 (8):1921-1936. DOI:10.22059/ijswr.2020.294238.668434
Jamieson, P.D., Porter, J.R. and Wilson, D.R., )1991(. A test of computer simulation model ARCWHEAT1 on wheat crop grown in NewZealand. Field Crop Res., 27: 337-350.DOI: 10.1016/0378-4290(91)90040-3
Karamouz, M., Ramazani, F. and Razavi, S. )2006(. Long-term forecasting of precipitation using meteorological signals: Application of artificial neural networks, 7th International Congress of Civil Engineering, Tehran, 11 p.
Khorshiddoust, A.M., Mofidi, A., Rasuly, A.A. and Azarm, K., )2016(. A Synoptic analysis for the occurrence of springtime heavy rainfall in the Northwest of Iran, Journal of Natural Environmental Hazards, 5(8):53-82.
Kim, C.G., Lee, J. Lee, J.E., Kim, N.W., and Kim, H., )2020(. Monthly Precipitation Forecasting in the Han River Basin, South Korea, Using Large-Scale Teleconnections and Multiple Regression Models, Water, 12, 1590; DOI:10.3390/w12061590.
Ma, C. and Iqbal, M., )1984(. Statistical comparison of solar radiation correlations monthly average global and diffuse radiation on horizontal surfaces. Sol. Energy 33:143-148.DOI: 10.1016/0038-092X(84)90231-7
Modaresi, F., Araghinejad, S. and Ebrahimi, K., )2018(. A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions, Water Resour Manage, 32:243-258. DOI: 10.1007/s11269-017-1807-2.
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, Journal of Agriculture Sciences Natural Resources, 15(2):217-224.
Nalley, D., Adamowski, J., Biswas, A., Gharabaghi, B. and 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.DOI: 10.1016/j.jhydrol.2019.04.024.
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, S.M.J. and Ghasemi, A.R., )2005(. The Influence of the Caspian Sea SSTs on winter and Spring Precipitation over Northern and Southwestern Parts of Iran, Journal of Water and Soil Science, 8(4):1-15.
Rahimi Khoob, A., )2011(. A Prediction of Maximum Monthly Precipitation Recorded at Ilam Meteorological Station Based on Persian Gulf and Red Sea Surface Temperature through Recordings Data Mining Method, Iranian Journal of Soin and Water Research, 42 (1):1-7.
Qian, H. and Xu, S. )2020(. Prediction of Autumn Precipitation over the Middle and Lower Reaches of the Yangtze River Basin Based on Climate Indices, Climate, 8, 53, DOI:10.3390/cli8040053
Sadatinejad, S.J., Shekari, M.R. and 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.DOI: 10.22059/IJE.2016.60027
Willmott, C.J. and Matsuura, K., )2006(. On the use of dimensioned measures of error to evaluate the performance of spatial interpolators. Int. J. Geogr. Inf. Sci. 20:89-102. DOI: 10.1080/13658810500286976