عنوان مقاله [English]
Monthly rainfall forecasting plays a major role in the water resources management and agroclimatic studies. The main purpose of this study is to assess the accuracy of NMME (North American Multi-Model Ensemble) in forecasting monthly rainfall in Sefidrood basin, North of Iran. For this purpose, the historical predicted data of NMME models for the period 1982 to 2017 were retrieved from, University of Columbia website, and compared with observed data obtained from the Iranian Meteorological Organization. The accuracy of NMME models predictions was evaluated by comparing them with the observed data, using statistical indices. The results showed that the single NMME model is not accurate, where the average value of determination coefficient (R2) was equal to 0.6. The models combination improved the accuracy of predictions, such that the determination coefficient increased to 0.7. Furthermore, for evaluation of the precipitation uncertainty, seventy-eight ensembles of the prediction models were investigated. The results of this evaluation showed that the models overestimated rainfall upto 80%. In addition, the uncertainty analysis of prediction models showed that the combination of models may reduce the uncertainty range.
Altunkaynak, A., Nigussie, T. A. 2017. Monthly water consumption prediction using season algorithm and wavelet transform–based models. Journal of Water Resources Planning and Management, 143(6), 04017011.
Barsugli, J. J., Vogel, J. M., Kaatz, L., Smith, J. B., Waage, M., Anderson, C. J. 2012. Two faces of uncertainty: Climate science and water utility planning methods. Journal of Water Resources Planning and Management, 138(5), 389-395.
Bruno Soares, M., Daly, M., Dessai, S. 2018. Assessing the value of seasonal climate forecasts for decision making. Wiley Interdisciplinary Reviews: Climate Change, 9(4), e523.
Dariane, A. B., Azimi, S. 2018. Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection. Journal of Hydroinformatics, 20(2), 520-532.
Efroymson, M. A. 1960. Multiple regression analysis," Mathematical Methods for Digital Computers, Ralston A. and Wilf, H. S., (eds.), Wiley, New York.
Fallon, A. L., Villholth, K. G., Conway, D., Lankford, B. A., Ebrahim, G. Y. 2019. Agricultural groundwater management strategies and seasonal climate forecasting: perceptions from Mogwadi (Dendron), Limpopo, South Africa. Journal of Water and Climate Change, 10(1), 142-157.
Gharde, K. D., Kothari, M., Mahale, D. M. 2016. Developed seasonal ARIMA model to forecast stream flow for Savitri Basin in Konkan Region of Maharshtra on daily basis. Journal of Indian Society Coastal Agricultural Research, 34, 110-119.
Khalili1, A., Rahimi, J., Bazrafshan, J. 2016. Quantitative projection of the probable impacts of climate change on date and damage risk of late spring frost during 21st century over Iran. Journal of Agricultural Meteorology, 4(2): 38-48. (In Farsi)
Kirtman, B. P., Min, D., Infanti, J. M., Kinter III, J. L., Paolino, D. A., Zhang, Q., Van Den Dool, H., Saha, S., Mendez, M. P., Becker, E., Peng, P. 2014. The North American multi-model ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bulletin of the American Meteorological Society, 95(4): 585-601.
Lang, Y., Ye, A., Gong, W., Miao, C., Di, Z., Xu, J., Liu, Y., Luo, L., Duan, Q. 2014. Evaluating skill of seasonal precipitation and temperature predictions of NCEP CFSv2 forecasts over 17 hydroclimatic regions in China. Journal of Hydrometeorology, 15(4): 1546-1559.
Ma, F., Luo, L., Ye, A., Duan, Q. 2018. Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in northwestern China. Hydrology and Earth System Sciences, 22(11): 5697-5709.
Morid, S., Smakhtin, V., Moghaddasi, M. 2006. Comparison of seven meteorological indices for drought monitoring in Iran. International Journal of Climatology: A Journal of the Royal Meteorological Society, 26(7): 971-985.
Najafi, H., Massah Bavani, P., Robertson, A. W. 2018. Evaluation of NMME seasonal temperature forecasts over Iran’s river basins. Journal of Agricultural Meteorology, 6(1): 19-30. (In Farsi)
Nazir, H. M., Hussain, I., Faisal., M., Shoukry, A. M., Gani, S., Ahmad, I. 2019. Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis. Complexity, doi: 10.1155/2019/2782715.
Reza, M., Harun, S., Askari, M. 2018. Streamflow forecasting in bukit merah watershed by using ARIMA and ANN. Portal: Jurnal Teknik Sipil, 9(1), doi: 10.30811/portal.v9i1.612.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y. T., Chuang, H.Y., Iredell, M., Ek, M. 2014. The NCEP climate forecast system version 2. Journal of Climate, 27(6): 2185-2208.
Schick, S., Rössler, O. K., Weingartner, R. 2018. Monthly streamflow forecasting at varying spatial scales in the Rhine basin. Hydrology and earth system sciences, 22(2), 929-942.
Shamir, E. 2017. The value and skill of seasonal forecasts for water resources management in the Upper Santa Cruz River basin, southern Arizona. Journal of Arid Environments, 137, 35-45.
Slater, L. J., Villarini, G., Bradley, A. A. 2017. Weighting of NMME temperature and precipitation forecasts across Europe. Journal of Hydrology, 552: 646-659.
Troccoli, A. 2010. Seasonal climate forecasting. Meteorological Applications, 17(3): 251-268.
Vitart, F., Buizza, R., Balmaseda, M. A., Balsamo, G., Bidlot, J. R., Bonet, A., Fuentes, M., Hofstadler, A., Molteni, F., Palmer, T. N., 2008. The new VAREPS monthly forecasting system: A first step towards seamless prediction. Quarterly Journal of the Royal Meteorological Society, 134(636): 1789-1799.
Vitart, F., Robertson, A. W., Anderson, D. L., 2012. Subseasonal to Seasonal Prediction Project: Bridging the gap between weather and climate. Bulletin of the World Meteorological Organization, 61(2), doi: 10.1038/s41612-018-0013-0.
Xu, J., Zhu, X., Zhang, W., Xu, X., Xian, J. 2009. Daily stream flow forecasting by artificial neural network in a large-scale basin. In 2009 IEEE Youth Conference on Information, Computing and Telecommunication (pp. 487-490). IEEE.
Xu, L., Chen, N., Zhang, X., Chen, Z., Hu, C., Wang, C. 2019. Improving the North American multi-model ensemble (NMME) precipitation forecasts at local areas using wavelet and machine learning. Climate Dynamics: 1-15.
Yuan, X. 2014. An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 2: The added value from climate forecast models. Hydrology and Earth System Science, 20: 2453–2466.