Prehistoric land use in southern Loess Plateau reconstructed from archeological data by a new developed model

Tuesday, 16 December 2014
Yanyan Yu, Haibin Wu and Zhengtang Guo, IGG Institute of Geology and Geophysics, Chinese Academy of Sciences, Key Laboratory of Cenozoic Geology and Environment, Beijing, China
Estimation of land use during the Holocene is crucial to understand impacts of human activity on climate change in preindustrial period. Until now it is still a key issue to reconstruct amount and spatial distribution of prehistoric land use due to lack of data. Most reconstructions are simply extrapolations of population, cleared land amount per person and land suitability for agriculture.

In this study, a new quantitative prehistoric land use model (PLUM) is developed based on semi-quantitative predictive models of archeological sites. The PLUM is driven by environmental and social parameters of archeological sites, which are objective evidences of prehistoric human activity, and produces realistic patterns of land use.

After successful validations of the model with modern observed data, the PLUM was applied to reconstruct land use from 8 to 4 ka B.P. in Yiluo and Wei valleys, southern Loess Plateau. Both of them are the most important agriculture origin centers in northern China. Results reveal that about 9% of land areas in both valleys have been used by human activity from 8 to 4 ka B.P., expanding from gentle slopes along the river to hinterlands of the valleys. The land cover was affected by increasing agricultural land use during the middle Holocene.

The extensive spreads of land use since 7 ka B.P. in both valleys were driven by the combined impacts of population increase and agriculture development, which was further favored by wet and warm climate conditions during middle Holocene; while the decreasing rates of land use expansions after 5 ka B.P. were mainly induced by improved agriculture technology.

With the scaling up of PLUM to larger regional or global levels by a greater use of archeological data, the impact of human land use on global change can be studied more accurately.