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Title: FLOODS AND DROUGHTS MODELING UNDER CLIMATE CHANGE SCENARIOS USING NEURAL NETWORKS

Author: N. El-Jabi, N. Turkkan and D. Caissie

Year: 2009

Publisher: European Water Resources Association (EWRA)

Description:

The purpose of this study is to investigate the impact of climate change on floods and droughts in New- Brunswick, Canada, using downscaled climate models and neural networks. Future climate data were extracted from the Canadian Coupled General Circulation Models (CGCM2) and the Hadley Circulation Model (HadCM3) with a multilayered feed-forward neural network models to predict the local variability in river discharge and extreme events. With projected high and low flow frequency calculations, it will be possible to compare current design criteria with
future scenarios that consider climate change. The simulations were computed using the generalized extreme value (GEV) distribution function, and the parameters of the distribution were estimated using L-moments method.