Pridiction of Sea Level Rise in the South of Iran Coastline: Evaluation of Climate Change Impacts
Subject Areas : Article frome a thesisHamid Goharnejad 1 , Abolfazl Shamsai 2 , sara نظیف 3 , Mahmood Zakeri Niri 4
1 - عضو هیات علمی دانشگاه آزاد اسلامی
2 - عضو هیات علمی
3 - استادیار دانشکده مهندسی عمران- پردیس دانشکدههای فنی دانشگاه تهران – تهران –ایران
4 - Department of civil Engineering, islamshahr Branch, islamic Azad university, Islamshahr, Iran
Keywords: Climate change scenarios, Sea-level rise, DWNN, DWANFIS,
Abstract :
The investigations in recent two decades have demonstrated the global sea level rise which is much more related to climate change phenomena and its impacts. In this study the impact of climate change on sea level rise at the southern coastal line of Iran is evaluated. The climatic output data of a GCM (General Circulation Model) named CGCM3 under climate change scenario of A1b was 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 are selected for sea level rise prediction using stepwise regression. Two models of Discrete Wavelet Artificial Neural Network (DWNN) and Discrete Wavelet Adaptive Neuro-Fuzzy Inference system (DWANFIS) are developed to explore the relationship between climatic variables and sea level changes. In these models wavelet is used to disaggregate the time series of climatic variables as well as sea level data into different components and then ANFIS/ANN are used to relate the disaggregated components of predictors and predictands to each other. The results of this study show a significant increase of sea level in future under climate change impacts which should be incorporated in coastal areas management. The selected model (Anfis-Haar), which had a high performance index, showed that the sea level changes from 48 centimeters in the west of the Persian Gulf to 16 centimeters in the east of the Oman Sea. The changes in shallow and enclosed waters appear to be greater than other parts of studied area.
1) اخیانی، محمود، چگینی، وحید و بیدختی، عباسعلی، 2011، مطالعه روند گرمایش زمین با بررسی تغییرات دمای سطحی در خلیج فارس و دریای عمان، دهمین همایش بین المللی سواحل، بنادر و سازه های دریایی
2) امیدوار، کمال، خسروی، یونس، 2009، تعیین روند و پیش بینی تغییرات دما و بارش شهر بوشهر، همایش بینالمللی خلیج فارس، 1 و 2 اردیبهشت : صص. 88 تا10
3) توکلی، مرضیه، قائدامینی اسدآبادی، حبیب اله و ناظم السادات، سیدمحمدجعفر، 2011، ارزیابی روند نوسانهای دمای پاییزه سطح آب پهنه شمال غرب اقیانوس هند، پنجمین کنفرانس سراسری آبخیزداری و مدیریت منابع آب و خاک کشور
4) خلیل آبادی. محمدرضا، محمدی. حمید و بیدختی. علی اکبر، 2004، بررسی تغییرات تراز دریا در حوضه شمالی خلیج فارس، ششمین همایش بین المللی سواحل، بنادر و سازه های دریایی
5) Adamowski, J., 2008a. Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis. Journal of Hydrology 353, 247–266.
6) Adamowski, J., 2008b. River flow forecasting using wavelet and cross-wavelet transform models. Hydrological Processes. 22, 4877–4891.
7) Armanfar, M., Goharnejad, H., Niri, M.Z. and Perrie, W., 2019. Assessment of coastal vulnerability in Chabahar Bay due to climate change scenarios. Oceanologia.
8) Bindoff, N.L., Willebrand, J., Artale, V., et al., 2007. Observations: oceanic climate change and sea level. In: Solomon, S., Qin, D., Manning, M., et al. (Eds.), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York, 387–429.
9) Burn, D.H., Cunderlik, J.M., 2004. Hydrological trends and variability in the Laird River basin. Hydrological Sciences Journal, 49 (1), 53–67.
10) Cannas, B., Fanni, A., See, L., Sias, G., 2006. Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Physics and Chemistry of the Earth. 31 (18), 1164–1171.
11) Coulibaly, P., Burn, D.H., 2005. Spatial and temporal variability of Canadian seasonal streamflows. Journal of Climate. 18, 191–210.
12) Defra, (for CITES policy issues) Trevor Salmon: Trevor.Salmon@defra.gsi.gov.uk
13) Goharnejad, H. and Eghbali, A.H., 2015. Forecasting the Sea Level Change in Strait of Hormuz. World Academy of Science, Engineering and Technology, International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering, 9(11), pp.1329-1332.
14) Goharnejad, H., Shamsai, A. and Hosseini, S.A., 2013. Vulnerability assessment of southern coastal areas of Iran to sea level rise: evaluation of climate change impact. Oceanologia, 55(3), pp.611-637.
15) Grinsted, A., Moore, J. C. & Jefrejeva, S. Clim. Dynam. 34, 461–472 (2009).
16) Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Xiaosu, D. (Eds.), Climate Change 2001: The scientific basis. Contribution of Working Group I to the Third Asssessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 639–693.
17) Intergovernmental Panel on Climate Change (IPCC), 2007. Climate Change 2007: Impact, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the IPCC. Cambridge University Press, UK, 976p.
18) Jevrejeva, S., Moore, J. C. & Grinsted, A. Geophys. Res. Lett. doi:10.1029/2010GL042947.
19) Karamouz, M., Tabesh, M., Nazif, S., Moridi, M., 2005. Estimation of Hydraulic Pressure in Water Networks Using Artificial Neural Networks and Fuzzy Logic. Journal of Water and Wastewater. No.53, 3–14.
20) Karamouz, M., Araghinejad, S., 2010. Advance Hydrology. Amir Kabir University Press. 523 p.
21) Kendall, M.G., 1975. Rank Correlation Methods. Charles Griffin, London.
22) Labat, D., 2005. Recent advances in wavelet analyses: Part 1. A review of concepts. Journal of Hydrology 314 (1–4), 275–288.
23) Liang, S.X., Li, M.C. and Sun, Z.C., 2008. Prediction models for tidal level including strong meteorologic effects using a neural network, Ocean Engineering, 35(7), 666-675.
24) Lu, R.Y., 2002. Decomposition of interdecadal and interannual components for North China rainfall in rainy season. Chinese Journal of Atmosphere 26, 611–624
25) Mallat, S., 1998. A Wavelet Tour of Signal Processing. Academic Press. Elsevier, UK.
26) Parisooj, P., Goharnejad, H. and Moazami, S., 2018. Rainfall-Runoff Hydrologic Simulation Using Adjusted Satellite Rainfall Algorithms, a Case Study: Voshmgir Dam Basin, Golestan Province. Iran-water resources research, Fall 2018 , Volume 14 , Number 3
27) Partal, T., Küçük, M., 2006. Long-term trend analysis using discrete wavelet components of annual precipitations measurements in Marmara region (Turkey). Physics and Chemistry of the Earth. 31, 1189–1200.
28) Pfeffer, W.T., Harper, J.T., O’Neel, S., 2008. Kinematic constraints on glacier contributions to 21st-century sea-level rise. Science 321(5894), 1340–1343.
29) Rahmstorf, S., 2007. A semi-empirical approach to projecting future sea-level rise. Science, 315(5810), pp.368-370.
30) Toufani, P., Mosaedi, A., Fakheri Fard, A., 2011. Prediction of Precipitation Applying Wavelet Network Model. Journal of Water and Soil. Vol. 25, No. 5, 1217-1226.
31) Vermeer, M. & Rahmstorf, S. Proc. Natl Acad. Sci. USA 106, 21527–21532 (2009).
32) Xingang, D., Ping, W., Jifan, C., 2003. Multi-scale characteristics of the rainy season rainfall and interdecadal decaying of summer monsoon in North China. Chinese Science Bulletin 48, 2730– 2734.
_||_