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Cross-scale interactions, nonlinearities, and forecasting catastrophic events.
Debra P C Peters1, Roger A Pielke, Brandon T Bestelmeyer
1U.S. Department of Agriculture Agricultural Research Service, Jornada Experimental Range, New Mexico State University, Las Cruces, NM 88003, USA. debpeter@nmsu.edu
Summary
Catastrophic events arise from complex cross-scale interactions. Understanding these nonlinear dynamics across time and space is crucial for predicting and mitigating disasters like wildfires and diseases.
Area of Science:
- Interdisciplinary science
- Complex systems analysis
- Predictive modeling
Background:
- Catastrophic events exhibit nonlinear behaviors driven by cross-scale interactions.
- Previous research focused on either temporal dynamics or spatial connectivity, neglecting their interplay.
- Existing approaches often remain within disciplinary silos.
Purpose of the Study:
- To develop a unified, interdisciplinary mathematical framework for understanding and forecasting nonlinear dynamics.
- To integrate temporal and spatial scales in analyzing catastrophic events.
- To bridge disciplinary gaps in the study of complex system failures.
Main Methods:
- Developed a general mathematical framework for cross-scale interaction analysis.
- Applied the framework to diverse datasets from ecology, epidemiology, and engineering.
- Recasted published data and incorporated new empirical evidence.
Main Results:
- Demonstrated the framework's utility in understanding nonlinear dynamics across scales.
- Illustrated the approach with examples of wildfires, desertification, infectious diseases, and structural failures.
- Showcased that minimizing catastrophic event likelihood requires considering cross-scale interactions.
Conclusions:
- A cross-scale, interdisciplinary approach is essential for predicting and managing catastrophic events.
- Effective strategies for mitigating disasters often involve counterintuitive decisions based on multi-scale analysis.
- The proposed framework advances predictive capabilities and experimental design for complex systems.