| description abstract | Sea-level rise (SLR) is one of the most damaging impacts of climate change. Rising sea levels lead to loss of coastal wetlands, coastal flooding, degradation of coastal ecosystems, and a general loss of quality of life. Due to its potential impacts on coastal management and on population health and safety, the impact of climate change on SLR has drawn significant attention in recent literature. SLR is associated with processes including glacial activity, ice-sheet melting, thermal expansion of sea water, hydrologic events such as increased or decreased land-based discharges, and local effects such as El Niño and La Niña, all of which are complexly linked to changes in global temperature. Unfortunately, many of these physical processes are not well understood in their relation to climate change, and the scientific knowledge required to represent them fully in predictive analysis is so complex that many current studies are shifting away from physical climate models to the application of empirical, semiempirical, and stochastic models. Another important aspect of these modeling studies is their characterization of the geographic pattern of SLR, which is used to evaluate the threat of SLR to specific geographic locations. In this application area, many of the current analysis techniques are also still in a fairly preliminary stage. The purpose of this study is to provide a review of this literature and to compare some of the successful attempts at modeling SLR. In closure, we also discuss a new dynamic system modeling approach that can successfully characterize the spatial variability of SLR. | |
