This paper outlines work currently being undertaken to reconstruct submerged archaeological landscapes off the north coast of Ireland. This research uses the recently-completed Joint Irish Bathymetric Survey (JIBS), which has provided full-coverage high-resolution multibeam bathymetric data for the sea-bed off the north coast. This data has been examined for evidence of past sea-level change and been used to produce palaeo-geographic reconstructions of the past landscape, which in turn have facilitated the identification of ten areas of high archaeological potential. The results presented here will form the basis of a future programme of archaeological survey and prospection.

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This paper describes Phase 1 of the project 'Archaeological Applications of the Joint Irish Bathymetric Survey (JIBS) Data', analysing bathymetric and backscatter data derived from multibeam surveys off the north coast of Ireland. In particular, the usability of the data for shipwreck detection, identification and site characterization is explored. In Phase 1, the data was screened for anomalous sea-bed features, which were subsequently described, catalogued and categorized according to their archaeological potential and cross-referenced against existing records. A planned second phase of this project will examine each anomaly in greater detail together with the local and regional hydrodynamic conditions.

We present early to mid-Holocene paleo-geographic reconstructions for the Ramore Head area (Northern Ireland). This coastal area is characterised by Mesolithic occupation (c. 10-6 ka) and preserved early-mid Holocene peats both on-and offshore. This paper improves on previous reconstructions by employing a backstripping methodology which removes accumulated recent deposits from identified buried paleolandsurfaces instead of using modern topography as an analogue to the past landscape. Paleo-landsurfaces are identified offshore from seismic profiles supplemented by cores, and onshore through legacy borehole records. The paleo-landsurface can be traced offshore to depths of -2 to -19 m and is buried by <5 m of modern sediment. It extends onshore under the coastal town of Portrush and is buried <2.5-10 m below modern ground level. The identified paleo-landsurface is combined with sea-level curves from recent Glacio-Isostatic-Adjustment models to reconstruct marine transgression during the early-mid-Holocene.Comparison is also made with reconstructions based on modern topography. Together, the identified paleo-landsurfaces and revised reconstructions can assist future site prospection on-and offshore and delimit high potential areas for heritage management. Revised reconstructions also allow placement of extant archaeology into a more accurate context of landscape change and help develop insights into localscale site location patterns.2

Coastal archaeological heritage is potentially vulnerable to increased erosion resulting from predicted future sea-level rise and increased storminess. As all sites cannot be protected, it is essential that heritage managers know which sites and landscapes are most at risk so they can prioritize resources and decision-making most effectively. One method of doing so is through desk-based modeling of coastal vulnerability. This article outlines the advantages and limitations of such an approach and demonstrates the application of a desk-based model to case studies from Newfoundland's coast. The rate of future sea-level rise around Newfoundland is complicated by its glacio-isostatic recovery since the 351 Kieran Westley et al.last ice age. The first step therefore in this assessment is to combine output from glacio-isostatic adjustment models with appropriate rates of global eustatic sea-level rise. Next, these data are integrated with existing information on coastal characteristics (topography, surficial geology, erosion rates) to assess coastal sensitivity to sea-level rise. Finally, overlay of known archaeological sites identifies those locations at greatest risk from destructive coastal changes. The results demonstrate the effectiveness of such models for regional-scale analyses but caution against the use of low resolution data to generate site-specific predictions.