The objective of this paper is to describe the process through which climate change scenarios were downscaled in an urban land use model and the results of this experimentation. The land use models (Urban Growth Model [UGM] and the Land Cover Deltatron Model [LCDM]) utilized in the project are part of the SLEUTH program which uses a probabilistic cellular automata protocol. The land use change scenario experiments were developed for the 31-county New York Metropolitan Region (NYMR) of the US Mid-Atlantic Region. The Intergovernmental Panel on Climate Change (IPCC), regional greenhouse gas (GHG) emissions scenarios (Special Report on Emissions Scenarios (SRES) A2 and B2 scenarios) were used to define the narrative scenario conditions of future land use change. The specific research objectives of the land use modeling work involving the SLEUTH program were threefold: (1) Define the projected conversion probabilities and the amount of rural-to-urban land use change for the NYMR as derived by the UGM and LCDM for the years 2020 and 2050, as defined by the pattern of growth for the years 1960-1990; (2) Down-scale the IPCC SRES A2 and B2 scenarios as a narrative that could be translated into alternative growth projections; and, (3) Create two alternative future growth scenarios: A2 scenario which will be associated with more rapid land conversion than found in initial projections, and a B2 scenario which will be associated with a slower level of land conversion. The results of the modeling experiments successfully illustrate the spectrum of possible land use/land cover change scenarios for the years 2020 and 2050. The application of these results into the broader scale climate and health impact study is discussed, as is the general role of land use/land cover change models in climate change studies and associated environmental management strategies.
[1]
A. Yeh,et al.
Measurement and monitoring of urban sprawl in a rapidly growing region using entropy
,
2001
.
[2]
S. Gaffin.
World Population Projections for Greenhouse Gas Emissions Scenarios
,
1998
.
[3]
Xia Li,et al.
Calibration of Cellular Automata by Using Neural Networks for the Simulation of Complex Urban Systems
,
2001
.
[4]
F. Wu,et al.
Simulating urban encroachment on rural land with fuzzy-logic-controlled cellular automata in a geographical information system
,
1998
.
[5]
Elisabete A. Silva,et al.
Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal
,
2002
.
[6]
Patrick L. Kinney,et al.
Simulating regional-scale ozone climatology over the eastern United States: model evaluation results
,
2004
.
[7]
Lily Parshall,et al.
Mitigation of the heat island effect in urban New Jersey
,
2005
.
[8]
Anthony Gar-On Yeh,et al.
Simulation of development alternatives using neural networks, cellular automata, and GIS for urban planning
,
2003
.
[9]
Keith C. Clarke,et al.
A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area
,
1997
.
[10]
G. Daily,et al.
Effects of household dynamics on resource consumption and biodiversity
,
2003,
Nature.