پیشبینی خشکسالی با استفاده از مدل‌های همادی آمریکای شمالی (NMME)در مناطق غربی ایران
محورهای موضوعی : مدیریت منابع آب
مهدی مقسمی
1
(گروه مهندسی منابع آب، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران.)
نرگس ظهرابی
2
(گروه مهندسی منابع آب، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران.)
حسین فتحیان
3
(گروه مهندسی منابع آب، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران.)
علیرضا نیکبخت شهبازی
4
(گروه مهندسی منابع آب، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران.)
محمدرضا یگانگی
5
(گروه حسابداری، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران.)
کلید واژه: پیشبینی بارش فصلی, مدل های همادی آمریکای شمالی, SPI, پیشبینی خشکسالی,
چکیده مقاله :
زمینه و هدف: خشکسالی به‌عنوان یک مخاطره طبیعی، تأثیرات زیادی در بخش های مختلف از جمله کشاورزی، منابع آب دارد و سالانه خسارات زیادی به این بخشها در سراسر دنیا تحمیل می کند. لذا باید راهکارهایی جهت کاهش خسارت خشکسالی صورت گیرد و در این بین برنامه ریزی و سازگاری با شرایط خشکسالی با استفاده از خروجی پیش بینی به هنگام خشکسالی جز مؤثرترین راهکارها به‌حساب می آید. با توجه به نیاز پیش بینی خشکسالی و محدود بودن مطالعات ارزیابی شاخص های خشکسالی به دست آمده از برون داد پیش بینی بارش مدل-های همادی آمریکای شمالی در ایران، در این پژوهش به بررسی این مدل ها در چهار حوضه آبریز کرخه، کارون بزرگ، حله و هندیجان-جراحی برای دوره 2018- 1982 پرداخته شد.روش پژوهش: در این پژوهش، ابتدا برونداد ماهانه مدل های مختلف همادی آمریکای شمالی و در افق های پیشبینی صفر تا 9 ماه و در دوره آماری 2018-1982 مورد ارزیابی قرار گرفت و سپس شاخص خشکسالی SPI محاسبه شده است. برای ارزیابی از مقایسه این داده ها با داده های GPCC استفاده شد. جهت ارزیابی از سه معیار کمی CC، RMSE و BIAS استفاده شد. همچنین جهت یکپارچه کردن مدل-های موجود از دو روش الف: میانگین حسابی بین مدل های موجود و ب: میانگین وزنی بین مدل ها با در نظر گرفتن نتایج ضریب همبستگی (CC) ارزیابی شده است. همچنین جهت ارزیابی شاخص خشکسالی SPI از دو معیار طبقه بندی شده POD و FAR و معیار کمی آماری CC استفاده شد.یافته‌ها: نتایج ارزیابی بارش مدل ها نشان داد که مدل های یکپارچه دارای عملکرد بهتری نسبت به مدل های انفرادی هستند و در این مدل یکپارچه نیز مدل وزن دهی شده عملکرد بهتری داشت. ارزیابی توزیع مکانی مدل های بارش نیز نشان داد که دو حوضه آبریز کارون بزرگ و هندیجان-جراحی در افق پیش بینی صفر ماه و حوضه آبریز هندیجان-جراحی در افق پیش بینی یک ماهه دارای عملکرد بهتری هستند. نتایج ارزیابی شاخص خشکسالی نشان داد که مدل های یکپارچه با وجود اینکه عملکرد بهتری در پیش بینی بارش داشتند اما در پیش بینی خشکسالی بهترین عملکرد متعلق به مدل های NASA-GMAO-062012 و CFSv2 است. همچنین نتایج نشان داد که پیش بینی شاخص خشکسالی در بازه های سه و شش ماه عملکرد بهتری نسبت به یک ماهه دارند. ارزیابی توزیع مکانی نیز نشان داد مدل ها در حوضه های جنوبی عملکرد بهتری دارند. به‌طور کلی می توان نتیجه گرفت که مدل های همادی آمریکای شمالی دارای عملکرد مناسبی در پیش بینی خشکسالی در بعضی نقاط و در افق های پیش بینی مشخص هستند، لذا باید در هر نقطه قبل از استفاده مورد ارزیابی قرار گیرند.نتایج: نتایج به دست آمده از ارزیابی بارش نشان داد که به‌طور کلی یکپارچه کردن برون داد مدل های دینامیکی باعث افزایش مهارت آن می شود و یکپارچه کردن در حالت وزنی (WeightedNMME) عملکرد بهتری نسبت به حالت غیر وزنی (NMME) دارد. در افق پیش-بینی صفر ماهه بین مدل‌های انفرادی نیز مدل NASA-GMAO-062012 بیشترین مهارت را از نظر شاخص ارزیابی CC دارد ولی در افق پیش بینی یک ماهه از نظر شاخص های ارزیابی CC، RMSE و BIAS بهترین عملکرد متعلق به مدل CFSv2 است. ارزیابی در شاخص های خشکسالی نشان داد که عملکرد مدل می تواند متفاوت از عملکرد آن‌ها در پیش بینی بارش باشد. به‌طور مثال مدل WeightedNMME با این که عملکرد مناسبی در پیش بینی خشکسالی دارد اما بهترین عملکرد در بین مدل ها در ماه های مختلف NASA-GMAO-062012 و CFSv2 داشتند. ارزیابی مکانی نیز نشان داد که حوضه های آبریز جنوبی دارای عملکرد بهتری نسبت بقیه حوضه ها هستند.
Background and Aim: Drought as a natural hazard significantly impacts various sectors such as agriculture and water resources and causes considerable damage to these sectors worldwide. Therefore, adaptation strategies should be taken to reduce drought damage, and in the meantime, planning and adaptation to drought conditions using drought forecasting is one of the most effective strategies. Due to the need for drought forecasting and the limited studies that evaluated drought indicators obtained from the rainfall forecast output from the North American Multi-Model Ensemble (NMME) in Iran. This study evaluated these models in four catchments of Karkheh, Karun, Heleh, and Hindijan-Jarahi for1982-2018.Method: In this study, the monthly output of different NMME ensembles were evaluated in the forecast leads of 0 to 9 months from 1982 to 2018, the SPI drought index was calculated. Comparison of these data with GPCC data was used for evaluation. Three quantitative criteria, including correlation coefficient, RMSE, and BIAS, were used for evaluation. Also, to integrate the existing models, two methods: a: Arithmetic mean between the existing models and B: Weighted average between the models have been evaluated by considering the correlation coefficient (CC) results. Also, two criteria (i.e., POD and FAR) and the quantitative statistical criterion (i.e., correlation coefficient) were used to evaluate the SPI drought index.Results: The results of the precipitation evaluation of the models showed that the integrated models have better performance than the individual models. In this integrated model, the weighted model also had better performance. Evaluation of spatial distribution of precipitation models also showed the excellent performance of NMME models in Karun and Hindijan-Jarahi catchments in the zero-month forecast lead and Hindijan-Jarahi catchments in the one-month forecast lead. The results of drought index evaluation showed that integrated models, although having better performance in precipitation forecasting, but in drought forecasting, the best performance belongs to NASA-GMAO-062012 and CFSv2 models. The results also showed that drought index forecasts in three and six-month periods have better performance than one month. Spatial distribution evaluation also showed that the models perform better in the southern basins. In general, it can be concluded that NMME models have good performance in predicting drought in some places and specific forecast leads, so they should be evaluated at each point before use.Conclusion: The results of precipitation evaluation showed that, in general, integrating the output of dynamic models increases its proficiency, and integration in weighted mode (WeightedNMME) performs better than the non-weighted model (NMME). According to the zero-month forecast among individual models, the NASA-GMAO-062012 model has the most skills in terms of the correlation coefficient. However, in the one-month forecast lead in terms of the correlation coefficient, RMSE and BIAS, the best performance belongs to the CFSv2 model. Evaluation of drought indices showed that the model's performance could be different from their performance in predicting rainfall. WeightedNMME, for example, performed well in NASA-GMAO-062012 and CFSv2 months, although they performed well in predicting drought. The spatial evaluation also showed that the southern catchments perform better than other basins.
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_||_AghaKouchak, A. (2014). A baseline probabilistic drought forecasting framework using standardized soil moisture index: application to the 2012 United States drought. Hydrology and Earth System Sciences, 18(7): 2485-2492.
AghaKouchak, A. (2015). A multivariate approach for persistence-based drought prediction: Application to the 2010–2011 East Africa drought. Journal of Hydrology, 526(1): 127-135.
Ajdari Moghadam, M., Khosravi, M., Hosseinpour Niknam, H. and jafari nedoshan A. (2012). Drought Index Prediction Using Fuzzy- Neural Model, Climatic Indices, Rainfall and Drought Index (Case Study: Zahedan). Journal of Geography and Development Iranian Journal 10(26): 61-72 [In Persian]
Awange, J. L., Mpelasoka, F., and Goncalves, R. M. (2016). When every drop counts: Analysis of Droughts in Brazil for the 1901-2013 period. Science of the Total Environment, 566(1): 1472-1488.
Bai, Y., Chen, Z., Xie, J., and Li, C. (2016). Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models. Journal of hydrology, 532(1): 193-206.
Belayneh, A., Adamowski, J., Khalil, B., and Ozga-Zielinski, B. (2014). Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models. Journal of Hydrology, 508(1): 418-429.
Dehban, H., Ebrahimi, K., Araghinejad, Sh. and Bazrafshan, J. (2019). Evaluation of NMME models in monthly rainfall forecasting (Case study: Sefidrood Basin). Journal of Iran-Water Resources Research 7(1): 3-12 [In Persian].
Gent, P. R., Yeager, S. G., Neale, R. B., Levis, S., and Bailey, D. A. (2010). Improvements in a half degree atmosphere/land version of the CCSM. Climate Dynamics, 34(6): 819-833.
Khajehei, S., Ahmadalipour, A., and Moradkhani, H. (2018). An effective post-processing of the North American multi-model ensemble (NMME) precipitation forecasts over the continental US. Climate dynamics, 51(1): 457-472.
Kirtman, B. P., and Min, D. (2009). Multimodel ensemble ENSO prediction with CCSM and CFS. Monthly Weather Review, 137(9): 2908-2930.
Kirtman, B. P., Min, D., Infanti, J. M., Kinter, J. L., Paolino, D. A., Zhang, Q., and Wood, E. F. (2014). The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bulletin of the American Meteorological Society, 95(4): 585-601.
Le, J. A., El-Askary, H. M., Allali, M., and Struppa, D. C. (2017). Application of recurrent neural networks for drought projections in California. Atmospheric research, 188: 100-106.
Han, P., Wang, P. X., and Zhang, S. Y. (2010). Drought forecasting based on the remote sensing data using ARIMA models. Mathematical and computer modelling, 51(11-12): 1398-1403.
Hao, Z., and AghaKouchak, A. (2014). A nonparametric multivariate multi-index drought monitoring framework. Journal of Hydrometeorology, 15(1): 89-101.
Hao, Z., AghaKouchak, A., Nakhjiri, N., and Farahmand, A. (2014). Global integrated drought monitoring and prediction system. Scientific data, 1(1): 1-10.
Hao, Z., Singh, V. P., and Xia, Y. (2018). Seasonal drought prediction: advances, challenges, and future prospects. Reviews of Geophysics, 56(1): 108-141.
Jahandid, M. and Shirvani, A. (2011). Drought Index Prediction based on standardized rainfall index using time series models over Fars province. Journal of Iranian Water Researches Journal 5(9): 19-27 [In Persian]
Ma, F., Yuan, X., and Ye, A. (2015). Seasonal drought predictability and forecast skill over China. Journal of Geophysical Research: Atmospheres, 120(16): 8264-8275.
Ma, F., Ye, A., Deng, X., Zhou, Z., Liu, X., Duan, Q., and Gong, W. (2016). Evaluating the skill of NMME seasonal precipitation ensemble predictions for 17 hydroclimatic regions in continental China. International Journal of Climatology, 36(1): 132-144.
Madadgar, S., AghaKouchak, A., Shukla, S., Wood, A. W., Cheng, L., Hsu, K. L., and Svoboda, M. (2016). A hybrid statistical‐dynamical framework for meteorological drought prediction: Application to the southwestern United States. Water Resources Research, 52(7), 5095-5110.
McKee, T. B., Doesken, N. J., and Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology (179-183).
McKee, T. B. 1995. Drought monitoring with multiple time scales. In Proceedings of 9th Conference on Applied Climatology, Boston, 1995.
Merryfield, W. J., Lee, W. S., Boer, G. J., Kharin, V. V., Scinocca, J. F., Flato, G. M., and Polavarapu, S. (2013). The Canadian seasonal to interannual prediction system. Part I: Models and initialization. Monthly weather review, 141(8): 2910-2945.
Moreira, E. E., Coelho, C. A., Paulo, A. A., Pereira, L. S., and Mexia, J. T. (2008). SPI-based drought category prediction using loglinear models. Journal of hydrology, 354(1-4): 116-130.
Mokhtarzad, M., Eskandari, F., Vanjani, N. J., and Arabasadi, A. (2017). Drought forecasting by ANN, ANFIS, and SVM and comparison of the models. Environmental earth sciences, 76(21): 1-10.
Najafi, H., Massah Bavani A.R., Irannejad P. and Robertson A. (2018). Application of North American Multi-Model Ensemble for Iran’s Seasonal Precipitation Forecasts. Journal of Iran-Water Resources Research 4: 28-38 [In Persian].
Nikbakht Shahbazi, A., Zahraie, B, and Nasseri, M. (2012). Seasonal Meteorological Drought Prediction Using Support Vector Machine. Journal of Water and Wastewater 23(2): 72-84 [In Persian].
Raziei, T. (2017). Drought forcasting in eastern and central arid and semi-arid regions of Iran using time series and Markov chainmodels. Journal of Water Engineering and management 8(4): 454-477 [In Persian].
SafarianZengir, V., Sobhani, B., and Asghari, S. (2020). Modeling and monitoring of drought for forecasting it, to reduce natural hazards atmosphere in western and north western part of Iran, Iran. Air Quality, Atmosphere and Health, 13(1): 119-130.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., and Becker, E. (2014). The NCEP climate forecast system version 2. Journal of climate, 27(6): 2185-2208.
Salvador, C., Nieto, R., Linares, C., Diaz, J., and Gimeno, L. (2019). Effects on daily mortality of droughts in Galicia (NW Spain) from 1983 to 2013. Science of The Total Environment, 662, 121-133.
Slater, L. J., Villarini, G., and Bradley, A. A. (2017). Weighting of NMME temperature and precipitation forecasts across Europe. Journal of Hydrology, 552: 646-659.
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