Development of Monthly Ensemble Precipitation Forecasting System in Sefidrud Basin, IRAN

Document Type : Research Paper


Irrigation and Reclamation Engineering Department, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran


Precipitation forecasting is one of the most important tools in water resources planning and management. Recently, new methods called atmospheric dynamic models have been used to predict many hydro-climate variables including precipitation. Before using the predictions of these models in planning and decision making, the accuracy of the mentioned predictions and their bias correction should be evaluated. Therefore the objective of this study is to ascertain the biases and to combine the results of precipitation forecasting with a set of global dynamic forecasting models. To achieve this aim, firstly the precipitation forecast results of each model were compared separately with the regional recorded precipitation data in the period of 1982 to 2017. Using this approach, the systematic errors were removed and corrected, i.e. using the quantile mapping method. This work was done for different forecast periods and also for different months. Furthermore, based on the accuracy of each model, a hybrid/multi-model prediction system was developed using Bayesian averaging method (BMA). The results showed that after the bias correction, using the quantitative mapping, at least one model among 78 prediction models have a relatively high correlation value of about 0.7. This result was recorded for the next one-month horizon. This correlation was increased to more than 0.8, by combining 78 predictive members, using Bayesian averaging method. Therefore, the accuracy of the predicted precipitation increases significantly using bias correction in tandem with combining the prediction models.


Abbasi, M, Araghinejad, Sh. and Ebrahimi, K. (2019). Evaluation of Moving Average Pre-processing Approach to Improve the Efficiency of Support Vector Regression Model for Inflow Prediction. Iranian Journal of Soil and Water Research, 50:1, 247-258 (In Farsi).
Abbaspur, Kc. (2009). SWAT-CUP2; SWAT calibration and uncertainty programs user manual. Federal institute of aquatic science and technology (Eawag), Swiss.
Ahmadalipour, A., Moradkhani, H., & Rana, A. (2018). Accounting for downscaling and model uncertainty in fine-resolution seasonal climate projections over the Columbia River Basin. Climate dynamics, 50(1-2), 717-733.
Bai, Y., Chen, Z., Xie, J., & Li, C. (2016). Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models. Journal of hydrology, 532, 193-206.
Barbero, R., Abatzoglou, J. T., & Hegewisch, K. C. (2017). Evaluation of statistical downscaling of North American multimodel ensemble forecasts over the western United States. Weather and Forecasting, 32(1), 327-341.
Courbariaux, M., Barbillon, P., Perreault, L., & Parent, É. (2018). Post-processing multi-ensemble temperature and precipitation forecasts through an Exchangeable Gamma Normal model and its Tobit extension. arXiv preprint arXiv:1804.09233.
Foroughi, F. and Araghinejad, Sh.  (2017). Long-lead streamflow forecasting using singular spectrum analysis in the Karkheh basin. Iranian Journal of Soil and Water Research, 48:2, 309-321 (In Farsi).
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., & Skaugen, T. E. (2012). Downscaling RCM precipitation to the station scale using quantile mapping--a comparison of methods. Hydrology & Earth System Sciences Discussions, 9(5).
Han, P., Wang, P. X., Zhang, S. Y., & Zhu, D. H. (2010). Drought forecasting based on the remote sensing data using ARIMA models. Mathematical and computer modelling, 51(11-12), 1398-1403.
Jia, L., Yang, X., Vecchi, G. A., Gudgel, R. G., Delworth, T. L., Rosati, A., ... & Msadek, R. (2015). Improved seasonal prediction of temperature and precipitation over land in a high-resolution GFDL climate model. Journal of Climate, 28(5), 2044-2062.
Khajehei, S., Ahmadalipour, A., & Moradkhani, H. (2018). An effective post-processing of the North American multi-model ensemble (NMME) precipitation forecasts over the continental US. Climate dynamics, 51(1-2), 457-472.
Kirtman, B. P., Min, D., Infanti, J. M., Kinter III, J. L., Paolino, D. A., Zhang, Q., ... & Peng, P. (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.
Kolachian, R., & Saghafian, B. (2019). Deterministic and probabilistic evaluation of raw and post processed sub-seasonal to seasonal precipitation forecasts in different precipitation regimes. Theoretical and Applied Climatology, 137(1-2), 1479-1493.
Le, J. A., El-Askary, H. M., Allali, M., & Struppa, D. C. (2017). Application of recurrent neural networks for drought projections in California. Atmospheric research, 188, 100-106.
Liu, L., Xiao, C., Du, L., Zhang, P., & Wang, G. (2019). Extended-Range Runoff Forecasting Using a One-Way Coupled Climate–Hydrological Model: Case Studies of the Yiluo and Beijiang Rivers in China. Water, 11(6), 1150.
Ma, F., Ye, A., Deng, X., Zhou, Z., Liu, X., Duan, Q., & 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.
Manganello, J. V., Cash, B. A., Hodges, K. I., & Kinter, J. L. (2017). Seasonal forecasts of North Atlantic tropical cyclone activity in the North American multi-model ensemble. Climate Dynamics, 1-16.
Modarresi, F., Araghinejad, Sh. and Ebrahimi. K. (2016). 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, 46:4, 597-607 (In Farsi).
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885-900.
Motovilov, Y. G., Gottschalk, L., Engeland, K., & Rodhe, A. (1999). Validation of a distributed hydrological model against spatial observations. Agricultural and Forest Meteorology, 98, 257-277.
Najafi, H., Massah Bavani, A., Irannejad, P., & Viliam Robertson, A. (2018). Developing Real-time Multi-Model Ensemble and Downscaling of Seasonal Precipitation Forecast Systems: Application of Canonical Correlation Analysis. Journal of the Earth and Space Physics (JESP), 44(1), 245-264.
Narapusetty, B., Collins, D. C., Murtugudde, R., Gottschalck, J., & Peters‐Lidard, C. (2018). Bias correction to improve the skill of summer precipitation forecasts over the contiguous United States by the North American multi‐model ensemble system. Atmospheric Science Letters, 19(5), e818.
Raftery, A. E., Gneiting, T., Balabdaoui, F., & Polakowski, M. (2005). Using Bayesian model averaging to calibrate forecast ensembles. Monthly weather review, 133(5), 1155-1174.
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.
Strazzo, S., Collins, D. C., Schepen, A., Wang, Q. J., Becker, E., & Jia, L. (2019). Application of a Hybrid Statistical–Dynamical System to Seasonal Prediction of North American Temperature and Precipitation. Monthly Weather Review, 147(2), 607-625.
Voisin, N., Schaake, J. C., & Lettenmaier, D. P. (2010). Calibration and downscaling methods for quantitative ensemble precipitation forecasts. Weather and Forecasting, 25(6), 1603-1627.
Xu, L., Chen, N., Zhang, X., & Chen, Z. (2018). An evaluation of statistical, NMME and hybrid models for drought prediction in China. Journal of hydrology, 566, 235-249.
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.
Zhao, T., Zhang, Y., & Chen, X. (2019). Predictive performance of NMME seasonal forecasts of global precipitation: A spatial-temporal perspective. Journal of Hydrology, 570, 17-25.