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作者

来源

LANDSCAPE AND URBAN PLANNING,Vol.167,P.128-135

语言

英文

关键字

CLIMATE-CHANGE; LAND-USE; SUITABILITY ANALYSIS; DECISION-SUPPORT; UNCERTAINTY; MANAGEMENT; FRAMEWORK; OPTIMIZATION; SOFTWARE; SCENARIO

作者单位

[Nino-Ruiz, Marcos; Bishop, Ian D.] Univ Melbourne, Dept Infrastruct Engn, Melbourne, Vic 3010, Australia. [Nino-Ruiz, Marcos; Bishop, Ian D.] Cooperat Res Ctr Spatial Informat, Melbourne, Vic, Australia. [Pettit, Christopher J.] Univ NSW, City Futures Res Ctr, Sydney, NSW 2052, Australia. Bishop, ID (reprint author), Univ Melbourne, Dept Infrastruct Engn, Melbourne, Vic 3010, Australia.; Bishop, ID (reprint author), Cooperat Res Ctr Spatial Informat, Melbourne, Vic, Australia. E-Mail: mninoruiz@gmail.com; i.bishop@unimelb.edu.au; c.pettit@unsw.edu.au

摘要

The ability to anticipate global environmental changes will significantly reduce the biophysical, social and economic costs associated with eventual adaptation. An abstract modelling process often supports evidence based decision making. Nonetheless, there are inherent difficulties for stakeholders in understanding complex scenario modelling. It is important to develop communication systems that support understanding of complex spatial decision models. This research used a Land Use Allocation (LUA) process, in the context of future agricultural land use under climate change scenarios, as a study in complex environmental modelling. The primary objective was to identify interactive options that can reduce the difficulty stakeholders have in understanding such an environmental model. A Spatial Model Steering (SMS) exploratory framework enabled users to explore the effects of climate change on land suitability, as a key aspect of LUA, and thus increase their perception of the influence of key factors. Within this framework, a user can visually steer the key climate, and climate response, related factors (rainfall, market price, and carbon price) of the LUA model, explore and compare "what if' future land use opportunities by adjusting these factors and visualize the spatial distribution of land suitability outcomes. The research compared the SMS approach with traditional methods of model output presentation and established that, with this approach, users develop both increased understanding of the key factors governing the underlying models and greater awareness of the uncertainty in the outcomes. This result provides a basis for the future use of complex spatial decision models within public debate.