Model Integration Techniques for Sustainability Decision-Making

点击翻译按钮获取中文摘要

Earth system models provide detailed simulations of the physical climate system and how it responds to human activities, such as emission of greenhouse gases. However, there is a large gap between current modelling capabilities, and the needs of decision-makers who must develop evidence-based responses to climate change. To close this gap, we need the ability to bring together disparate computational models to reason about systems interactions that span multiple areas of expertise across the natural and social sciences, and we need better tools to characterization the nature of uncertainty in the models and the implications of this uncertainty for decision-making. The proposed research will investigate how to construct, validate and use integrated models of systems-of-systems to provide decision support for the challenge of building resilient, sustainable communities for a climate changed world. This will require an exploration of inter-dependencies across natural and social systems, and the ability to model and reason about complex dynamical behaviour and integrate model components built by diverse groups of experts, each with its own assumptions and limitations. I I I will begin by comparing the model integration techniques used by climate scientists (which I have already studied in depth) with approaches taken in other areas. For example, climate modelling began with simulations of the thermodynamics of atmosphere and oceans, but has expanded to incorporate interactions with other systems, such as ice sheets, vegetation and soils, atmospheric chemistry, and human activities. Similarly, the models used in urban planning must take into account the interaction of land use, demographics, transport, energy, water, waste, etc. An initial assessment of urban systems modelling indicates that integrated modelling is currently rare, but increasingly in demand to support longer term decision-making for sustainability, as the interaction of behaviours across different urban systems affect the resilience and sustainability of the city as a whole. The study will focus on the software tools and techniques used for model integration, and the scientists' practices around testing and deploying integrated models, including the communication of uncertainty to downstream users of models and model results. To tackle this challenge, I will apply a mixed-methods approach, using multiple case studies to provide a compare-and-contrast approach, action research to work with existing modelling communities to tackle specific challenges, and pilot studies to evaluate new solutions. The long term aim of this research is build a stronger foundation for evidence-based decision-making for sustainability, and to help overcome disciplinary barriers and research silos for trans-disciplinary problems. A guiding hypothesis is that model integration initiatives can accelerate the move to trans-disciplinary thinking, as they provide a focus for the process of discovering and resolving differences in assumptions, terminology, and methodology between scientists with different disciplinary backgrounds. In this sense, computational models act as detailed, explicit descriptions of scientific theories, and integrating them stimulates the development of a deeper cross-disciplinary understanding. Example applications for this work include policymaking for mitigation and adaptation to climate change, urban planning for sustainable cities, and sustainable agriculture. Expected outcomes of this research include a typology of integration technologies and software architectures for scientific models, identification of best practices for scientific validation of integrated models, and, in the longer term, software frameworks for predictive modelling of systems-of-systems.