Over the last 8000 years, the Fertile Crescent of the Near East has seen the emergence of cities, states and empires. Climate fluctuations are generally considered to be a significant factor in these changes because in pre-industrial societies they directly relate to food production and security. In the short term, ‘collapse’ events brought about by extreme weather changes such as droughts have been blamed for declines in population, social complexity and political systems. More broadly, the relationships between environment, settlement and surplus drive most models for the development of urbanism and hierarchical political systems.
Studies seeking to correlate social and climatic changes in the past tend either to focus on highly localised analyses of specific sites and surveys or to take a more synthetic overview at much larger, even continental, scales. The CLaSS project will take a ground breaking hybrid approach using archaeological data science (or ‘big data’) to construct detailed, empirical datasets at unprecedented scales. Archaeological settlement data and archaeobotanical data (plant and tree remains) will be collated for the entire Fertile Crescent and combined with climate simulations derived from General Circulation Models using cutting edge techniques. The resulting datasets will represent the largest of their kind ever compiled, covering the period between 8000BP and 2000BP and an area of 600,000km2.
Collecting data at this scale will enable us to compare population densities and distribution, subsistence practices and landscape management strategies to investigate the question: What factors have allowed for the differential persistence of societies in the face of changing climatic and environmental conditions? This ambitious project will provide insights into the sustainability and resilience of societies through both abrupt and longer term climate changes, leveraging the deep time perspective only available to archaeology.
Field of science
- /humanities/history and archaeology/archaeology
- /natural sciences/computer and information sciences/data science
- /social sciences/other social sciences/social sciences interdisciplinary/sustainable development
- /natural sciences/computer and information sciences/data science/big data
Call for proposal
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