Investigations on the impact of projected sea level rise on the groundwater regime in a coastal aquifer

Abstract

Global warming caused by anthropogenic activities and the resulting climate change is newlinea major concern of contemporary society. Projections from various climate models newlineindicate that global temperatures are on continuous ascent. The Intergovernmental newlinePanel on Climate Change (IPCC) and several other scientific bodies have concluded newlinethat this will result in a rise in water levels in the oceans, with consequent impacts on newlinecoastal ecosystems, including aquifers. Sea level rise (SLR) impacts coastal regions newlineadversely, triggering inundation and coastal erosion; it also changes the location of the newlineseawater-freshwater interface. Worldwide, coastal aquifers are under tremendous newlinepressure, critically impairing the quality and adversely affecting the quantity of newlinegroundwater. In this context, it is extremely important to investigate the climate change newlinedriven impacts on coastal aquifers. Precise estimates of projected sea levels in the future newlinemust be achieved to assess these impacts so that appropriate and sustainable newlinemanagement strategies can be devised and implemented to tackle and mitigate the newlineadverse impacts. newlineTo achieve accurate estimates of future sea level, the climatic variables that newlinesignificantly influence sea level have to be identified. In this research, the climatic newlinevariables substantially influencing regional sea level, the so-called predictors, were newlineidentified, and these variables were utilized to develop statistical downscaling models newlinefor downscaling sea level projections from GCMs employing three machine learning newlineapproaches. In this study, projections of predictors for the future extracted from seven newlinePhase V Coupled Model Inter-comparison Project (CMIP5) Global Climate Models newline(GCMs), viz., GISS-E2-H, CanESM2, MIROC-ESM, ACCESS1-0, CNRM-CM5, newlineGFDL-CM3, and CMCC-CM were used for downscaling sea level projections up to the newlineyear 2050, employing the machine learning approach that performed the best. newline