Environment-Aware Digital Twins: Incorporating Weather and Climate Information to Support Risk-Based Decision-Making

Abstract

Abstract Digital twins are a transformative technology that can significantly strengthen climate adaptation and mitigation decision-making. Through provision of dynamic, virtual representations of physical systems, making intelligent use of multidisciplinary data, and high-fidelity simulations they equip decision-makers with the information they need, when they need it, marking a step change in how we extract value from data and models. While digital twins are commonplace in some industrial sectors, they are an emerging concept in the environmental sciences and practical demonstrations are limited, partly due to the challenges of representing complex environmental systems. Collaboration on challenges of mutual interest will unlock digital twins’ potential. To bridge the current gap between digital twins for industrial sectors and those of the environment, we identify the need for “environment aware” digital twins (EA-DT) that are a federation of digital twins of environmentally sensitive systems with weather, climate, and environmental information systems. As weather extremes become more frequent and severe, the importance of building weather, climate, and environmental information into digital twins of critical systems such as cities, ports, flood barriers, energy grids, and transport networks increases. Delivering societal benefits will also require significant advances in climate-related decision-making, which lags behind other applications. Progress relies on moving beyond heuristics, and driving advances in the decision sciences informed by new theoretical insights, machine learning and artificial intelligence. To support the use of EA-DTs, we propose a new ontology that stimulates thinking about application and best practice for decision-making so that we are resilient to the challenges of today’s weather and tomorrow’s climate.