Authors: Jing Gao*, University of Delaware, Brian O'Neill, University of Denver
Topics: Spatial Analysis & Modeling, Land Use and Land Cover Change, Global Change
Keywords: Spatiotemporal Simulations, Urban, Agricultural, Land Use, Shared Socioeconomic Pathways, Climate Change
Session Type: Paper
Start / End Time: 8:00 AM / 9:40 AM
Room: Madison A, Marriott, Mezzanine Level
Presentation File: No File Uploaded
Long-term spatially-explicit modeling of urban and agricultural land use are essential for studying interactions between human societies and climate change, while urban and agricultural uses often compete for the most fertile land available. To examine potential two-way influence between climate and land use, this paper takes a data-science approach to producing plausible long-term spatial scenarios of coordinated urban and agricultural land use that are consistent with the Shared Socioeconomic Pathways (SSPs) and can serve as inputs to climate modeling. The urban model is a new capacity. It draws on newly available time series of fine-spatial-resolution urban land change data spanning the past 30 years, as well as best available spatial population and environmental variables. The model functions at multiple spatial scales, including national, regional, and 1/8-degree grid-cell levels. From existing data on contemporary urbanization, the model identified three distinct development styles reflecting different socioeconomic trajectories (i.e. developed, steadily developing, and rapidly developing). These trajectories were used in combination with Monte Carlo experiments to generate a range of national amounts of urban land throughout the 21st century according to the SSP narratives. At grid cell level, the model reproduces subnational variations in the spatiotemporal dynamics of contemporary urbanization. The urban land model is linked with an existing agricultural land model reflecting competitions between the two land uses. Besides a novel methodology, the paper showcases the resulting set of coordinated urban and agricultural land use projections, and highlights prominent trends and patterns.