Relative Sea Level Changes in Maldives and Vulnerability of Land Due to Abnormal Coastal Inundation

Oceanic Islands in the Pacific and Indian Oceans have extremely small land areas, usually less than 500 km2, with maximum height about 4 m above sea level. The Republic of Maldives is an independent island nation in the Indian Ocean south of Sri Lanka which stretches vertically in the Indian Ocean from 07° 06'N - 0° 42'S. The land area of this island country is about 300 km2, and none of Maldives' 1190 islands has an elevation more than 3 m above sea level. In fact the Maldives has the distinction of being the flattest country on earth, making it extremely vulnerable to the effects of global warming. Of the south Asian countries, the Maldives is the most vulnerable nation, facing severe consequences as a result of global warming and sea level rise (SLR). Because of their obvious vulnerability to SLR, the Government of Maldives is very much concerned about climate change. As global warming and the related SLR is an important integrated environmental issue, the need of the hour is to monitor and assess these changes. The present article deals mainly with the analysis of the tidal and Sea Surface Temperature (SST) data observed at Male and Gan stations along the Maldives coast in the northern and southern hemispheres, respectively. The objective of the analysis is to study the trends of these parameters. Trend analysis is also performed on the corresponding air temperature data of both stations. The results show that Maldives coastal sea level is rising in the same way (rising trend) as the global sea level. The mean tidal level at Male has shown an increasing trend of about 4.1 mm/year.Similarly at Gan, near the equator,it has registered a positive trend of about 3.9 mm/year.Sea level variations are the manifestations of various changes that are taking place in the Ocean-Atmosphere system. Therefore, the variations in SST and air temperature are intimately linked to sea level rise. It is found that SST and air temperature have also registered an increasing trend at both stations. The evidence of rising trends suggest that careful future monitoring of these parameters is very much required. Tropical cyclones normally do not affect the Maldives coast. However, due to its isolated location, the long fetches in association with swells generated by storms, that originated in the far south have resulted in flooding. Thus the rising rate of sea level with high waves and flat topography have increased the risk of flooding and increased the rate of erosion and alteration of beaches.     

Practical aspects of wavefield separation of two-component surface seismic data based on polarization and slowness estimates

The processing of multicomponent seismic data, carried out individually on the different wavetypes (P-, S- and converted waves), should result in an improved image of the subsurface. We examine the wavefield‐separation method proposed by Cho and Spencer . We discuss practical aspects related to the separation of interfering waves on two-component surface seismic data and illustrate these using synthetic data. A sliding spatial window is used for analysis. The choice of its width represents a trade-off between stabilizing the method in the presence of random noise and ensuring a good spatial resolution. No a priori knowledge of the subsurface is required, but locally, the characteristic parameters of the waves, i.e. horizontal slowness and polarization, are assumed to be constant inside the analysis window. These parameters are estimated at each frequency, but a statistical analysis provides a more robust estimate, especially in the presence of random noise. This approach also solves the problem of eigenvalue sharing and switching. Additional smoothing of the estimates according to a model may further improve the results. The width of the analysis window may be decreased if the waves inside the data window differ significantly in amplitude. The dominant wave in each case is separated from the lower-amplitude waves and subtracted from the data. This novel iterative approach thereby allows for the isolation of low-amplitude events.