An Evaluation of Climate Change Impacts on Extreme Sea Level Variability: Coastal Area of New York City

详细信息    查看全文

• 作者：Mohammad Karamouz (1) (3)
  Zahra Zahmatkesh (2)
  Sara Nazif (3)
  Ali Razmi (4)
  
• 关键词：Climate change ; Extreme sea level ; Large scale climate signals ; Artificial neural network
• 刊名：Water Resources Management
• 出版年：2014
• 出版时间：September 2014
• 年：2014
• 卷：28
• 期：11
• 页码：3697-3714
• 全文大小：969 KB
• 参考文献：1. Abdul-Karim MD, Chowdhury JU (1995) A comparison of four distributions used in flood frequency analysis in Bangladesh. Hydrol Sci J 40(1):55-6 CrossRef
  2. Abida H, Ellouze M (2007) Probability distribution of flood flows in Tunisia. Hydrol Earth Syst Sci Discuss 4:957-81 CrossRef
  3. Alila Y, Mtiraoui A (2002) Implications of Heterogeneous Flood-frequency Distributions on Traditional Stream-discharge Prediction Techniques. Hydrol Process 16:1065-084. doi:10.1002/hyp.346 CrossRef
  4. Blazkova S, Beven K (1997) Flood frequency prediction for data limited catchments in the Czech Republic using a stochastic rainfall model and topmodel. J Hydrol 195:256-78 CrossRef
  5. Bowman, M. J., (2000) “Rising sea levels and storm surges-beyond 2080- Stony Brook University
  6. Burn DH (1990) Evaluation of regional flood frequency analysis with a region of influence approach. Water Resour Res 26(10):2257-265 CrossRef
  7. Cayan, D, Tyree, M., Pierce, D., and Das, T., (2012) “Climate change and sea level rise scenarios for california vulnerability and adaptation assessment. California Energy Commission- CEC-500-2012-008
  8. Chakravart IM, Laha RG, Roy J (1967) Handbook of Methods of Applied and Statistical Inference. Wiley, New York, pp 392-94
  9. Chowdhury M, Rashed D, Shin Chu P, Schroeder T, Colasacco N (2007) Seasonal Sea-Level Forecasts by Canonical Correlation Analysis—an Operational Scheme for The U.S.-Affiliated Pacific Islands. Int J Climatol 27(10):1389-402 CrossRef
  10. Clark G, Moser S, Ratick S, Dow K (1998) Assessing the vulnerability of coastal communities to extreme storms: the case of Revere, MA, USA- Mitig Adapt Strat Global Change 3:59-2 CrossRef
  11. Cunnane, C. (1989) statistical distributions for flood frequency analysis. WMO operational hydrology report no. 33. World Meteorological Organisation, 41 Giuseppe Motta, Casa Postale no. 5, CH 1211 Geneva, Switzerland
  12. Cutter L, Mitchell JT, Scott MS (2000) “Revealing the vulnerability of people and places: a case study of Georgetown County- South Carolina. Ann Assoc Am Geogr 90:713-37 CrossRef
  13. Cover TM, Thomas JA (1991) Elements of information theory. John Wiley & Sons, New York
  14. David SD, Baish S, Morrow BH (1999) Uncovering the hidden costs of coastal hazard. Environment 41(8):10-9 CrossRef
  15. De Oliveira MF, Ebecken NF, De Oliveira JLF, De Azevedo Santos I (2009) Neural network model to predict a storm surge. Journal of Applied Meteorology & Climatology 48(1):143-55 CrossRef
  16. Gornitz V, Couch S, Hartig E (2002) Impacts of sea level rise in the New York City metropolitan area. Global and Planetary Changes 32:61-8 CrossRef
  17. Haan CT (1994) Statistical methods in hydrology. Iowa State University Press, Ames
  18. Haktanir T (1991) Statistical modelling of maximum flows in Turkish rivers. Hydrol Sci J 36(4):367-89 CrossRef
  19. Hao, J., (2005) “Input selection using mutual Information - applications to time series prediction- Helsinki University of Technology, Master Thesis
  20. Hassoun M (1995) Fundamentals of Artificial Neural Networks. MA, MIT Press, Cambridge, p 544
  21. Huang W, Murray C, Kraus N, Rosati J (2003) Development of a regional neural network for coastal water level predictions. Ocean Eng 30:2275-295. doi:10.1016/S0029-8018(03)00083-0
  22. Iliadis LS, Spartalis S (2009) Hydrological neural modeling aided by support vector machines. Workshops Proceedings AIAI-2009:249-59
  23. Interagency Advisory Committee on Water Data, (1982) Guidelines for determining flood-flow frequency: Bulletin 17B of the Hydrology Subcommittee, Office of Water Data Coordination, U.S. Geological Survey, Reston, Va., 183 p
  24. IPCC (1996) Climate Change 1995. In: Houghton JT (ed) The Science of Climate Change. Contribution of the Working Group I to the Second Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York, p 572
  25. IPCC (2001) Summary for policymakers: climate change 2001: impacts, adaptation, and vulnerability. A report of working group II of the intergovernmental panel on climate change; available at www.ipcc.ch/pub/ wg2SPMfinal.pdf
  26. IPCC (2007) Climate Change 2007: Impacts, adaptation and vulnerability. Cambridge University Press, Cambridge
  27. Karim F, Mimura N (2008) Impacts of climate change and sea-level rise on cyclonic storm surge floods in Bangladesh. Glob Environ Chang 18:490-00 CrossRef
  28. Meehl G. A, Hibbard K. A, (2007) “Strategy for climate change stabilization experiments with AOGCMs and ESMs- Aspen Global Change Institute 2006 session. Earth system models: the next generation (Aspen, Colorado, July 30–August 5, 2006). Aspen, USA: international geosphere–biosphere programme; p. 37
  29. Mileti D (1999) Disasters by design: a reassessment of natural hazards in the United States- The Joseph Henry Press, Washington
  30. Morrow BH (1999) Identifying and mapping community vulnerability- Disasters 23:1-8 CrossRef
  31. Mutua FM (1994) The use of the Akaike information criterion in the identification of an optimum flood frequency model. Hydrol Sci J 39(3):235-44 CrossRef
  32. Pashova L, Popova S (2011) Daily sea level forecast at tide gauge Burgas, Bulgaria using artificial neural networks. Original Research Article Journal of Sea Research 66(2):154-61
  33. Saf B (2008) Application of index procedures to flood frequency analysis in Turkey. J Am Water Resour Assoc (JAWRA) 44(1):37-7. doi:10.1111/j.1752-1688.2007.00136.x CrossRef
  34. Shannon CE, Weaver W (1949) The mathematical theory of communication. The University of Illinois Press, Illinois
  35. Singh VP, Strupczewski WG (2002) On the status of flood frequency analysis. Hydrol Process 16:3737-740. doi:10.1002/hyp.5083 CrossRef
  36. Ultsch A, Roske F (2002) Self-organizing feature maps predicting sea levels. Inf Sci 144(1-):91-25. doi:10.1016/S0020-0255(02)00203-7
  37. Vogel RM, Thomas WO, McMahon TA (1993) Flood-flow frequency model selection in the south western United States. Wat Resour Plan Manage ASCE 119(3):353-66 CrossRef
  38. Wallis, J. R. (1988) Catastrophes, computing and containment: living in our restless habitat
  39. Walsh K, McInnes K, McBride J (2012) Climate change impacts on tropical cyclones and extreme sea levels in the South Pacific -A regional assessment- Glob Planet Chang 80-1:149-64 CrossRef
  40. Wenhong L, Li L, Fu R, Deng Y, Wang H (2010) Changes to the North Atlantic subtropical high and its role in the intensification of summer rainfall variability in the Southeastern United States. Am Meteorol Soc 24(5):1499-506
  41. Wu S, Yarnal B, Fisher A (2002) Vulnerability of coastal communities to sea-level rise: a case study of Cape May County. New Jersey, USA. Clim Res 22:255-70 CrossRef
  
• 作者单位：Mohammad Karamouz (1) (3)
  Zahra Zahmatkesh (2)
  Sara Nazif (3)
  Ali Razmi (4)
  
  1. Polytechnic School of Engineering, New York University, Brooklyn, NY, USA
  3. School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
  2. School of Civil Engineering, University of Tehran, Tehran, Iran
  4. Department of Water Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
  
• ISSN：1573-1650

文摘

Climate change has resulted in sea level rise and increasing frequency of extreme storm events around the world. This has intensified flood damage especially in coastal regions. In this study, a methodology is developed to analyze the impacts of climate change on sea level changes in the coastal regions utilizing an artificial neural network model. For simulation of annual extreme sea level, climate signals of Sea Surface Temperature, Sea Level Pressure and SLP gradient of the study region and some characteristic points are used as predictors. To select the best set of predictors as neural network model input, feature selection methods of MRMR (Minimum Redundancy Maximum Relevance) and MI (Mutual Information) are used. Future values of the selected predictors under greenhouse gas emission scenarios of B1, A1B and A2 are used in the developed neural network model to project water level for the next 100?years. Sea levels with different recurrence intervals are determined using frequency analysis of historical and projected water level as well, and the impact of climate change in extreme sea level is investigated. The developed methodology is applied to New York City to determine the coastal region vulnerability to water level changes. The results of this study show remarkable increase in sea level in the New York City, which is an indicative of coastal areas vulnerability and the need to take strategic actions in dealing with climate change.