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Advances in Meteorology
Volume 2013 (2013), Article ID 297926, 9 pages
Research Article

Interactions between Climate, Socioeconomics, and Land Dynamics in Qinghai Province, China: A LUCD Model-Based Numerical Experiment

1Institute of Geographic and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2Center for Chinese Agricultural Policy, Chinese Academy of Sciences, Beijing 100101, China
3School of Mathematics and Physics, China University of Geosciences (Wuhan), Wuhan 430074, China

Received 29 August 2013; Accepted 20 November 2013

Academic Editor: Burak Güneralp

Copyright © 2013 Xiangzheng Deng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This simulation-based research produces a set of forecast land use data of Qinghai Province, China, applying the land use change dynamics (LUCD) model. The simulation results show that the land use pattern will almost keep being consistent in the period from 2010 to 2050 with that in 2000 in Qinghai Province. Grassland and barren or sparsely vegetated land will cover more than 80% of the province’s total area. The land use change will be inconspicuous in the period from 2010 to 2050 involving only 0.49% of the province’s land. The expansion of urban and built-up land, grassland, and barren or sparsely vegetated land and the area reduction of mixed dryland/irrigated cropland and pasture, water bodies, and snow or ice will dominate land use changes of the case study area. The changes of urban and built-up land and mixed dryland/irrigated cropland and pasture will slow down over time. Meanwhile, the change rates of water bodies, snow and ice, barren or sparsely vegetated land, and grassland will show an inverted U-shaped trajectory. Except for providing underlying surfaces for RCMs for future climate change assessment, this empirical research of regional land use change may enhance the understanding of land surface system dynamics.