Local modeling

The science made simple: modeling multi-sectoral dynamics


Some components included in an integrated human-natural systems model to capture feedbacks between human-caused factors and natural systems at regional and global scales. Credit: DOE

Earth system models view the world as a complex web of interactions between many different forces. For example, natural water supply is important for both farmers and power plant operators. Decisions made by farmers and power plant operators, in turn, affect rivers and streams.

Multi-Sector Dynamic Modeling (MSD) is used by scientists to explore the interactions and interdependencies between human and natural systems. These systems are complex and adaptive. They interact and co-evolve in response to short-term shocks as well as long-term influences and stresses. Interactions occur everywhere, from the local to the global scale, and influences often transfer from one scale to another. Interactions between these systems often react nonlinearly to stresses. These systems can experience cascading effects or failures after crossing tipping points.

By improving our understanding of interdependent systems, we better understand the potential trajectories, vulnerabilities, responses and resilience of these systems. It would also help us better understand the structure, function and evolution of the complex human and environmental landscapes that embody these systems.

MSDs often include representations of energy, water and land systems, infrastructure, natural resources, economies, technologies, populations, climate, and weather patterns and extremes. MSD’s strength—and its greatest challenge—lies in how it connects socio-economic, physical, engineering, and Earth system components into risk and decision-making frameworks.

Facts about modeling multi-sectoral dynamics

DOE Multi-Sector Dynamic Modeling and Science Office

The Office of Science (SC) supports MSD in part by promoting the wide scientific use of these methods. For example, SC encourages scientists to share data and develop open source models. These steps help the tools meet the needs of many users working with many different systems.