Predicting instabilities in transient landforms and interconnected ecosystems

Area of Science:

• Earth System Science
• Dynamical Systems Theory
• Ecological Stability

Background:

• Earth systems often exhibit multiple stable states, risking abrupt shifts.
• Current stability assessments require stationary data, involving complex pre-processing.
• Existing methods struggle with non-stationary, noisy, or diverse datasets.

Purpose of the Study:

• To develop a novel method for quantifying the stability of periodically forced systems.
• To enable stability analysis of Earth system components without data pre-processing.
• To assess the stability of real-world systems such as glaciers and rainforests.

Main Methods:

• Application of Floquet Multipliers to analyze periodically forced systems.
• Utilizing diverse time-series data without requiring trend or seasonality removal.
• Testing the method on synthetic data, vegetation models, and real-world datasets.

Main Results:

• Successfully predicted glacier surge onset using surface velocity data.
• Identified spatially explicit destabilization patterns in the Amazon rainforest.
• Demonstrated robustness to varying noise levels and data merging.

Conclusions:

• Floquet Multipliers offer a robust, pre-processing-free approach to stability assessment.
• The method is applicable to a wide range of spatio-temporal systems, including climate and ecosystems.
• Enables early detection of potential catastrophic shifts in Earth systems.