Development of A Novel Conceptual and Calculative Method for the Prediction of Tide within the Bay of Bengal



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Submitted Jul 27, 2022
Published Aug 23, 2022
10.24018/ejmath.2022.3.4.134

Abstract viewed = 166 times

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Ebb and flow of seawater at regular intervals under the influence of gravitational forces from outside the earth is called tides. This study sheds light on how to measure tides through a new kind of innovative method. This method can be used to measure tides at new locations based on a known tide station. Some parameters play an important role in measuring tides, such as datum information, mean sea level (MSL), mean high water springs (MHWS), mean low water springs (MLWS) and so on. This fancy method is a computational process that relies on observable data and other factors. This innovative method of measuring tides helps to get accurate information in less time and at less cost. From this study we find that the results obtained in our study are in good agreement with the results obtained by other methods and the observed data.
Keywords: Bay of Bengal, Coast, Datum, MSL, Tide

References

  1. Al Mohit MA, Yamashiro M, Hashimoto N, Mia MB, Ide Y, Kodama M. Impact assessment of a major river basin in Bangladesh on storm surge simulation. J Mar Sci Eng. 2018; 6(3): 99.
     Google Scholar
  2. Paul GC, Ismail AIM. Numerical modeling of storm surges with air bubble effects along the coast of Bangladesh. Ocean Eng. 2012; 42: 188–94.
     Google Scholar
  3. Paul GC, Ismail AIM. Tide-surge interaction model including air bubble effects for the coast of Bangladesh. J Franklin Inst. 2012; 349(8): 2530–46.
     Google Scholar
  4. Paul GC, Ismail AIM. Contribution of offshore islands in the prediction of water levels due to tide-surge interaction for the coastal region of Bangladesh. Nat Hazards. 2013; 65(1): 13–25.
     Google Scholar
  5. Mohit AA, Ide Y, Yamashiro M, Hashimoto N. A comparative study of two different numerical methods on storm surge. Proc 9th Int Conf APAC 2017. 2018; (213039): 163–74.
     Google Scholar
  6. Roy GD. Inclusion of off-shore islands in a transformed coordinates shallow water model along the coast of Bangladesh. Environ Int. 1999; 25(1): 67–74.
     Google Scholar
  7. Takagi H, Thao ND, Esteban M. Tropical Cyclones and Storm Surges in Southern Vietnam. Coast Disasters Clim Chang Vietnam Eng Plan Perspect. 2014; 3–16.
     Google Scholar
  8. Paul GC, Ismail AIM, Karim MF. Implementation of method of lines to predict water levels due to a storm along the coastal region of Bangladesh. J Oceanogr. 2014; 70(3): 199–210.
     Google Scholar
  9. Al Mohit MA, Towhiduzzaman M. A numerical estimate of water level elevation due to a cyclone associated with a different landfall angle. Sains Tanah. 2022; 19(1): 33–41.
     Google Scholar
  10. Pearson GA, Brawley SH. Reproductive ecology of Fucus distichus (Phaeophyceae): An intertidal alga with successful external fertilization. Mar Ecol Prog Ser. 1996; 143(1–3): 211–23.
     Google Scholar
  11. Maricle BR, Lee RW. Root respiration and oxygen flux in salt marsh grasses from different elevational zones. Mar Biol. 2007; 151(2): 413–23.
     Google Scholar
  12. Davies MS, Edwards M, Williams GA. Movement patterns of the limpet Cellana grata (Gould) observed over a continuous period through a changing tidal regime. Mar Biol. 2006; 149(4): 775–87.
     Google Scholar
  13. Koch H. Desiccation Resistance of the Supralittoral Amphipod Traskorchestia Traskiana (Stimpson, 1857). Crustaceana. 1989; 56(2): 162–75.
     Google Scholar
  14. Richardson CA, Ibarrola I, Ingham RJ. Emergence pattern and spatial distribution of the common cockle Cerastoderma edule. Mar Ecol Prog Ser. 1993; 99(1–2): 71–81.
     Google Scholar
  15. Kellmeyer K, Salmon M. Hatching rhythms of Uca thayeri rathbun: Timing in semidiurnal and mixed tidal regimes. J Exp Mar Bio Ecol. 2001; 260(2): 169–83.
     Google Scholar
  16. Dethier MN, Williams SL, Freeman A. Seaweeds under stress: Manipulated stress and herbivory affect critical life-history functions. Ecol Monogr. 2005; 75(3): 403–18.
     Google Scholar
  17. Stachowicz JJ, Best RJ, Bracken MES, Graham MH. Complementarity in marine biodiversity manipulations: Reconciling divergent evidence from field and mesocosm experiments. Proc Natl Acad Sci USA. 2008; 105(48): 18842–7.
     Google Scholar
  18. Matassa CM, Trussell GC. Landscape of fear influences the relative importance of consumptive and nonconsumptive predator effects. Ecology. 2011; 92(12): 2258–66.
     Google Scholar
  19. Li H, Lei G, Zhi Y, Bridgewater P, Zhao L, Wang Y, et al. Phenotypic responses of Spartina anglica to duration of tidal immersion. Ecol Res. 2011; 26(2): 395–402.
     Google Scholar
  20. Miller LP, Matassa CM, Trussell GC. Climate change enhances the negative effects of predation risk on an intermediate consumer. Glob Chang Biol. 2014; 20(12): 3834–44.
     Google Scholar
  21. Pincebourde S, Sanford E, Helmuth B. An intertidal sea star adjusts thermal inertia to avoid extreme body temperatures. Am Nat. 2009; 174(6): 890–7.
     Google Scholar
  22. Pawlowicz R, Beardsley B, Lentz S. Classical tidal harmonic analysis including error estimates in MATLAB using TDE. Comput Geosci. 2002; 28(8): 929–37.
     Google Scholar
  23. Thurman HV. Introductory Oceanography. Seventh Edition. New York, NY: Macmillan, 1994: 252-276.
     Google Scholar