Vulnerability assessment of southern coastal areas of Iran to sea level rise: evaluation of climate change impact

Recent investigations have demonstrated global sea level rise as being due to climate change impact. Probable changes in sea level rise need to be evaluated so that appropriate adaptive strategies can be implemented. This study evaluates the impact of climate change on sea level rise along the Iranian south coast. Climatic data simulated by a GCM (General Circulation Model) named CGCM3 under two-climate change scenarios A1b and A2 are used to investigate the impact of climate change. Among the different variables simulated by this model, those of maximum correlation with sea level changes in the study region and least redundancy among themselves are selected for predicting sea level rise by using stepwise regression. Two Discrete Wavelet artificial Neural Network (DWNN) models and a Discrete Wavelet Adaptive Neuro-Fuzzy Inference System (DWANFIS) are developed to explore the relationship between selected climatic variables and sea level changes. In these models, wavelets are used to disaggregate the time series of input and output data into different components. ANFIS/ANN are then used to relate the disaggregated components of predictors and predictand (sea level) to each other. The results show a significant rise in sea level in the study region under climate change impact, which should be incorporated into coastal area management.