SCC-PG: Coupling Digital Twins with Multisector Models to Build Economic and Infrastructure Resilience in Rural Gateway Communities

Popular destinations, such as national parks, and their gateway communities are co-dependent, complex systems. The remote and fragile economies are exposed to compounding stressors, such as disasters, peak visit seasons, and infrastructure and workforce capacity limitations. Future long-term resiliency planning is critical; however, existing efforts have been siloed, non-comprehensive, and limited to short planning horizons. Multisectoral dynamic (MSD) models can provide long-term projections and explore interdependencies among multiple sectors under future uncertainties in climate and socioeconomics. However, MSD approaches may be out of reach for community planners. Digital Twins (DTs) have recently become popular as a real-life extension of models through virtual reality. We propose to link MSD models with DTs to assist gateways in resilience planning. This approach provides an extension and communication strategy while soliciting community feedback as a “reality-check” for models. Our model framework provides a new approach to model outreach that can be extended to address long-term resilience planning in any community. Our research supports institutional diversity and engages seven community leaders across six institutions to serve as advisors. Curriculum and training will accompany workshops to enhance utilization of our tools, as well as support workforce development.

U.S. National Parks, such as Yellowstone National Park (YNP), and their gateway communities are dynamic, co-dependent, and co-evolving systems. Integrated socioeconomic and environmental planning efforts, achieved through coupled modeling frameworks, are needed for holistic regional planning for YNP and communities. We propose to develop Multisector Dynamic (MSD) models, as emulators of long-term future system and sector interdependencies, and integrate these with digital twins (DTs), which provide short-term virtual realities of MSD projections, thereby producing a cycle of information feedback between data science and system dynamics. Specifically, we will project future population, economic activity, land use, and infrastructures (buildings, transportation, and water utilities), in conjunction with flood and wildfire risk. These high-resolution projections will be coupled with DTs to provide real-life simulations of alternative futures, including three-dimensional visualizations of future gateway villages, dynamic traffic patterns and congestion, infrastructure depictions, and wildlife migration corridors. In this way, DTs coupled with MSDs assist in communicating complex model results to practitioners, enhance community participation, and provide a conduit to improve process understanding, assess model plausibility, and increase the likelihood of identifying resilient outcomes.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.