Understanding environmental complexity by reducing it

Beyond its applicability to specific systems, reduced-complexity modeling can constitute an alternate approach to scientific inquiry that is beginning to gain more traction in communities of environmental scientists. Reduced-complexity models can be used to explore the dynamics of complex-systems—as in exploratory statistical studies—and gain an intuitive understanding of their functioning and sensitivities. They are also engines for hypothesis generation, can set priorities for field work, and formulaically facilitate interdisciplinary collaboration. Together with other participants in the NSF-funded Art and Science of Reduced Complexity Modeling workshop, chaired by Professor Larsen, we are working to establish priorities for future research and achieve a synthetic understanding of how this technique can be used to improve our ability to predict the responses of complex environmental systems to change. One of the key outcomes of the workshop was insight into how reduced-complexity modeling can solve challenges in working across the disciplinary boundaries of geomorphology and ecology related to processes that occur on vastly different spatial and temporal scales. Another outcome was the generation of hypotheses regarding certain types of landscape dynamics (e.g., alternate stable states, threshold behavior) and the general types of interactions that make certain landscapes more prone to these nonlinearities than others. A third working-group priority is to refine and test reduced-complexity techniques for modeling surface-water flows through rivers and floodplains.

Participants of the Art and Science of Reduced Complexity Modeling Workshop, held in Boulder in March 2013.

Participants of the Art and Science of Reduced Complexity Modeling Workshop, held in Boulder in March 2013.

Examples of reduced-complexity flow-vegetation-geomorphology models.

Examples of reduced-complexity flow-vegetation-geomorphology models.