Forecasting the Sea Level Change in Strait of Hormuz
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Forecasting the Sea Level Change in Strait of Hormuz

Authors: Hamid Goharnejad, Amir Hossein Eghbali

Abstract:

Recent investigations have demonstrated the global sea level rise due to climate change impacts. In this study, climate changes study the effects of increasing water level in the strait of Hormuz. The probable changes of sea level rise should be investigated to employ the adaption strategies. The climatic output data of a GCM (General Circulation Model) named CGCM3 under climate change scenario of A1b and A2 were used. Among different variables simulated by this model, those of maximum correlation with sea level changes in the study region and least redundancy among themselves were selected for sea level rise prediction by using stepwise regression. One of models (Discrete Wavelet artificial Neural Network) was developed to explore the relationship between climatic variables and sea level changes. In these models, wavelet was used to disaggregate the time series of input and output data into different components and then ANN was used to relate the disaggregated components of predictors and input parameters to each other. The results showed in the Shahid Rajae Station for scenario A1B sea level rise is among 64 to 75 cm and for the A2 Scenario sea level rise is among 90 t0 105 cm. Furthermore, the result showed a significant increase of sea level at the study region under climate change impacts, which should be incorporated in coastal areas management.

Keywords: Climate change scenarios, sea-level rise, strait of Hormuz, artificial neural network, fuzzy logic.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1109944

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References:


[1] H. Goharnejad, A. Shamsai, S. A. Hosseini, “Vulnerability assessment of southern coastal areas of Iran to sea level rise: evaluation of climate change impact,” OCEANOLOGIA, 55 (3), 2013. pp. 611–637.
[2] Intergovernmental Panel on Climate Change, 2000. Climate Change: The IPCC Scientific Assessment. Cambridge University Press, New York, NY.
[3] IPCC. Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) (Cambridge Univ. Press, Cambridge, UK, and New York, 2007).
[4] Labat, D., 2005. Recent advances in wavelet analyses: Part 1. A review of concepts. Journal of Hydrology 314 (1–4), 275–288.
[5] Lu, R. Y., 2002. Decomposition of interdecadal and interannual components for North China rainfall in rainy season. Chinese Journal of Atmosphere 26, 611–624.
[6] Makarynskyy. O., Makarynska. D., Kuhn. M., Featherstone. W.E., 2004, Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia, Estuarine, Coastal and Shelf Science. 351–360.
[7] Mallat, S., 1998. A Wavelet Tour of Signal Processing. Academic Press. Elsevier, UK.