Evaluation and investigation of archaeological opportunities in critical topographic zones within the eastern sector of the North Sea.

The focus of WP2 is to reconstruct the landscape evolution in the eastern North Sea by using legacy and new geophysical, geological and geotechnical data. The WP will further provide accurate sea level modelling to support landscape reconstructions across the project. It will identify and investigate areas of potential archaeological interest, working closely with the AI models produced in other workpackages, to assess coastal, ecosystem and human responses to rising sea levels.

For SUBNORDICA, the eastern North Sea represents the area where we known the least. Besides of a scarcity of contextual archaeological data similar to the southern regions of the North Sea, there is generally a lack of knowledge about how the landscape looked liked and how it evolved since the last glaciation and up to the flooding. There are further many unknowns about how the geological processes during and after the flooding affected the preservation and burial of the terrestial landscape that SUBNORDICA is targetting. 

The Eastern North Sea holds unique topographic zones that are of critical importance for the assessment of archaeology across the northern European coastal shelf.

• Dogger Bank, a distinct topographical high and later isolated island which may have provided a unique environment attractive for human occupation
• Elbe Palaeo Valley, a major landscape element that could have constrained migration patterns across north-western Europe

WP2 will provide information to fill the major knowledge gap of the eastern North Sea’s submerged landscape and further utilize the geological characteristics of the Elbe Palaeo Valley and the Dogger Bank to evaluate how landscapes, coastal ecosystems and human populations adapted to rising sea level. The work package will use existing and new geological and geophysical data for palaeolandscape and palaeoenvironmental reconstruction using established modelling schemes and machine learning approaches developed in other work packages.