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. 1. Introduction Land use change and the resulting changes in land surface characteristics are recognized drivers of climate change [1–3]. The biogeochemical impacts of land use change on the climate through changing atmospheric concentrations of greenhouse gases (GHGs) have been of great concern. For instance, the GHGs from agriculture land uses are estimated to account for 10–20% of the total global anthropogenic emissions [4]. The gross CO2 emissions from tropical deforestation are roughly equivalent to 40% of global fossil fuel emissions from 1990 to 2007 [5]. Besides lands under management, natural land cover types such as wetlands and primeval forest are also found to be large sources and sinks of GHGs [6]. Land use change affects climate by not only impacting GHG emissions but also land surface properties. Urban heat island (UHI) is one of the most noticeable effects of land surface property changes on local climate change [7–9]. Land use change may result in changes of thermal properties (albedo, thermal conductivity, and emissivity) and further affect the surface energy budgets as well as atmospheric circulations [10–13]. Consequently, land use data should play a central role in climate change assessment. While the importance of land use change in climate change modeling has been fully realized, most regional climate
References
[1]
R. A. Pielke Sr., “Land use and climate change,” Science, vol. 310, no. 5754, pp. 1625–1626, 2005.
[2]
X. Z. Deng, C. H. Zhao, and H. M. Yan, “Systematic modeling of impacts of land use and land cover changes on regional climate: a review,” Advances in Meteorology, vol. 2013, Article ID 317678, 11 pages, 2013.
[3]
F. Wu, J. Y. Zhan, H. M. Yan, C. C. Shi, and J. Huang, “Land cover mapping based on multisource spatial data mining approach for climate simulation: a case study in the farming-pastoral ecotone of North China,” Advances in Meteorology, vol. 2013, Article ID 520803, 12 pages, 2013.
[4]
P. H. Verburg, K. Neumann, and L. Nol, “Challenges in using land use and land cover data for global change studies,” Global Change Biology, vol. 17, no. 2, pp. 974–989, 2011.
[5]
Y. Pan, R. A. Birdsey, J. Fang et al., “A large and persistent carbon sink in the world's forests,” Science, vol. 333, no. 6045, pp. 988–993, 2011.
[6]
B. Kayranli, M. Scholz, A. Mustafa, and ?. Hedmark, “Carbon storage and fluxes within freshwater wetlands: a critical review,” Wetlands, vol. 30, no. 1, pp. 111–124, 2010.
[7]
Y. Du, Z. Xie, Y. Zeng, Y. Shi, and J. Wu, “Impact of urban expansion on regional temperature change in the Yangtze River Delta,” Journal of Geographical Sciences, vol. 17, no. 4, pp. 387–398, 2007.
[8]
M. Stathopoulou, A. Synnefa, C. Cartalis, M. Santamouris, T. Karlessi, and H. Akbari, “A surface heat island study of Athens using high-resolution satellite imagery and measurements of the optical and thermal properties of commonly used building and paving materials,” International Journal of Sustainable Energy, vol. 28, no. 1–3, pp. 59–76, 2009.
[9]
R. J. Qu, X. L. Cui, H. M. Yan, E. J. Ma, and J. Y. Zhan, “Impacts of land cover change on the near-surface temperature in the North China Plain,” Advances in Meteorology, vol. 2013, Article ID 409302, 12 pages, 2013.
[10]
G. L. Makokha and C. A. Shisanya, “Temperature cooling and warming rates in three different built environments within Nairobi City, Kenya,” Advances in Meteorology, vol. 2010, Article ID 686214, 5 pages, 2010.
[11]
R. Bornstein and Q. Lin, “Urban heat islands and summertime convective thunderstorms in Atlanta: three case studies,” Atmospheric Environment, vol. 34, no. 3, pp. 507–516, 2000.
[12]
K. J. Anderson-Teixeira, P. K. Snyder, T. E. Twine, S. V. Cuadra, M. H. Costa, and E. H. Delucia, “Climate-regulation services of natural and agricultural ecoregions of the Americas,” Nature Climate Change, vol. 2, no. 3, pp. 177–181, 2012.
[13]
E. J. Ma, A. P. Liu, X. Li, F. Wu, and J. Y. Zhan, “Impacts of vegetation change on the regional surface climate: a scenario-based analysis of afforestation in Jiangxi Province, China,” Advances in Meteorology, vol. 2013, Article ID 796163, 8 pages, 2013.
[14]
Y. Cai, L. Tan, H. Cheng et al., “The variation of summer monsoon precipitation in central China since the last deglaciation,” Earth and Planetary Science Letters, vol. 291, no. 1–4, pp. 21–31, 2010.
[15]
I. Jankov, W. A. Gallus Jr., M. Segal, B. Shaw, and S. E. Koch, “The impact of different WRF model physical parameterizations and their interactions on warm season MCS rainfall,” Weather and Forecasting, vol. 20, no. 6, pp. 1048–1060, 2005.
[16]
M. Z. Jacobson and J. E. Ten Hoeve, “Effects of urban surfaces and white roofs on global and regional climate,” Journal of Climate, vol. 25, no. 3, pp. 1028–1044, 2012.
[17]
C. M. Rozoff, W. R. Cotton, and J. O. Adegoke, “Simulation of St. Louis, Missouri, land use impacts on thunderstorms,” Journal of Applied Meteorology, vol. 42, pp. 716–738, 2003.
[18]
X. Guo, D. Fu, and J. Wang, “Mesoscale convective precipitation system modified by urbanization in Beijing City,” Atmospheric Research, vol. 82, no. 1-2, pp. 112–126, 2006.
[19]
K. Trusilova, M. Jung, G. Churkina, U. Karsten, M. Heimann, and M. Claussen, “Urbanization impacts on the climate in Europe: numerical experiments by the PSU-NCAR mesoscale model (MM5),” Journal of Applied Meteorology and Climatology, vol. 47, no. 5, pp. 1442–1455, 2008.
[20]
X. Z. Deng, Modeling the Dynamics and Consequences of Land System Change, Springer, 2011.
[21]
M. Batty, Y. Xie, and Z. Sun, “Modeling urban dynamics through GIS-based cellular automata,” Computers, Environment and Urban Systems, vol. 23, no. 3, pp. 205–233, 1999.
[22]
C. A. Jantz and S. J. Goetz, “Analysis of scale dependencies in an urban land-use-change model,” International Journal of Geographical Information Science, vol. 19, no. 2, pp. 217–241, 2005.
[23]
J. V. Vliet, R. White, and S. Dragicevic, “Modeling urban growth using a variable grid cellular automaton,” Computers, Environment and Urban Systems, vol. 33, no. 1, pp. 35–43, 2009.
[24]
W. J. McConnell, “Agent-based models of land-use and land-cover change,” LUCC Report 6, 2001.
[25]
T. Zhang, J. Y. Zhan, J. Huang, R. Yu, and C. C. Shi, “An agent-based reasoning of impacts of regional climate changes on land use changes in the Three-River Headwaters Region of China,” Advances in Meteorology, vol. 2013, Article ID 248194, 9 pages, 2013.
[26]
F. Wu, X. Z. Deng, F. Yin, and Y. W. Yuan, “Projected changes of grassland productivity along the Representative Concentration Pathways during 2010–2050 in China,” Advances in Meteorology, vol. 2013, Article ID 812723, 9 pages, 2013.
[27]
H. Yu, Z. Zhang, and P. Zhang, “RS-and GIS-based evaluation and dynamic monitoring of land desertification in Qinghai Province,” Arid Zone Research, vol. 24, no. 2, pp. 153–158, 2007.
[28]
Y. Wei, C. Xu, S. Zhang, and L. Song, “Effects of climatic changes on biomass and eco-environments of natural grassland in Haibei region of Qinghai province,” Pratacultural Science, vol. 25, no. 2, pp. 12–17, 2008.
[29]
L. Wang, Y. Wei, and Z. Niu, “Spatial and temporal variations of vegetation in Qinghai Province based on satellite data,” Journal of Geographical Sciences, vol. 18, no. 1, pp. 73–84, 2008.
[30]
X. Deng, H. Su, and J. Zhan, “Integration of multiple data sources to simulate the dynamics of land systems,” Sensors, vol. 8, no. 2, pp. 620–634, 2008.
[31]
X. Deng, J. Huang, Q. Huang, S. Rozelle, and J. Gibson, “Do roads lead to grassland degradation or restoration? a case study in Inner Mongolia, China,” Environment and Development Economics, vol. 16, no. 6, pp. 751–773, 2011.
[32]
B. Güneralp and K. C. Seto, “Environmental impacts of urban growth from an integrated dynamic perspective: a case study of Shenzhen, South China,” Global Environmental Change, vol. 18, no. 4, pp. 720–735, 2008.
[33]
J. Schmidhuber and F. N. Tubiello, “Global food security under climate change,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 50, pp. 19703–19708, 2007.
[34]
J.-C. Castella, T. N. Trung, and S. Boissau, “Participatory simulation of land-use changes in the northern mountains of Vietnam: the combined use of an agent-based model, a role-playing game, and a geographic information system,” Ecology and Society, vol. 10, no. 1, pp. 1–32, 2005.
[35]
R. B. Matthews, N. G. Gilbert, A. Roach, J. G. Polhill, and N. M. Gotts, “Agent-based land-use models: a review of applications,” Landscape Ecology, vol. 22, no. 10, pp. 1447–1459, 2007.
[36]
M. Horridge and G. Wittwer, “The economic impacts of a construction project, using SinoTERM, a multi-regional CGE model of China,” China Economic Review, vol. 19, no. 4, pp. 628–634, 2008.
[37]
G. Wittwer and M. Horridge, “A multi-regional representation of China's agricultural sectors,” China Agricultural Economic Review, vol. 1, no. 4, pp. 420–434, 2009.