4.6 Article

Long-term changes in sea-level components in Latin America and the Caribbean

Journal

GLOBAL AND PLANETARY CHANGE
Volume 104, Issue -, Pages 34-50

Publisher

ELSEVIER
DOI: 10.1016/j.gloplacha.2013.02.006

Keywords

inter-annual variability; Latin America and the Caribbean; sea-level components; sea-level rise; storm surge; non-stationary extremes

Funding

  1. University of Cantabria
  2. Spanish Ministry of Economy [CSD2007-00067, CTM201015009]
  3. Economical Commission for Latin America and the Caribbean (ECLAC) from the United Nations (UN)
  4. Spanish Agency on Climate Change (OECC)
  5. Spanish Ministry of Agriculture, Food and Environment [CSD2007-00067, CTM201015009]

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When considering the threat of rising sea-levels, one must take into account not only the changes in the Mean Sea-Level, but also storm surges and changes in extreme events which may also have a bearing on coastal problems. In this study, we combine different components of the total sea-level (astronomical tide, monthly mean sea-level and storm surges) to explain changes detected in the region of Latin America and the Caribbean. Methods based on non-stationary extreme value analysis were applied to storm surge and total sea elevations monthly maxima for the last six decades, while long-term trends in Mean Sea-level were computed from both local regression and a trend-EOF technique. In addition, the relative importance of each factor contributing to the total sea-level is explored by means of defining each statistical distribution. The analysis demonstrates that concerns should be focused on the different components of sea-level in the various areas of the region. For example, changes in the storm surge levels are a key stressor in the Rio de la Plata area, while the increase in the extreme total sea-levels in the tropical region and the influence of inter-annual variability on its western coast are the prominent factors. Results show that a clear correspondence between Mean Sea-Level and the Nino3 climate index can be found through a simple regression model, explaining more than 65% of the variance for a representative location on the Peruvian coast. (C) 2013 Elsevier B.V. All rights reserved.

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