1
Department of Civil Engineering,University of Tehran
2
Department of Civil and Environmental Engineering,Amirkabir University of Technology
3
Department of Civil Engineering,Amirkabir University of Technology
Abstract
Abstract. Long lead rainfall prediction is important in the management and operation of water
resources and many models have been developed for this purpose. Each of the developed models has
its special strengths and weaknesses that must be considered in real time applications. In this paper, eld
and General Circulation Models (GCM) data are used with the Statistical Downscaling Model (SDSM)
and the Arti cial Neural Network (ANN) model for long lead rainfall prediction. These models have been
used for the prediction of rainfall for 5 months (from December to April) in a study area in the south
eastern part of Iran. The SDSM model considers climate change scenarios using the selected climate
parameters in rainfall prediction, but the ANN models are driven by observed data and do not consider
physical relations between variables. The results show that SDSM outperforms the ANN model.
Nazif, S., Karamouz, M., Fallahi, M., Rahimi Farahani, M. (2009). Long Lead Rainfall Prediction Using Statistical Downscaling and Arti cial Neural Network Modeling. Scientia Iranica, 16(2), -.
MLA
S. Nazif; M. Karamouz; M. Fallahi; M. Rahimi Farahani. "Long Lead Rainfall Prediction Using Statistical Downscaling and Arti cial Neural Network Modeling". Scientia Iranica, 16, 2, 2009, -.
HARVARD
Nazif, S., Karamouz, M., Fallahi, M., Rahimi Farahani, M. (2009). 'Long Lead Rainfall Prediction Using Statistical Downscaling and Arti cial Neural Network Modeling', Scientia Iranica, 16(2), pp. -.
VANCOUVER
Nazif, S., Karamouz, M., Fallahi, M., Rahimi Farahani, M. Long Lead Rainfall Prediction Using Statistical Downscaling and Arti cial Neural Network Modeling. Scientia Iranica, 2009; 16(2): -.
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