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Predicting Instabilities In Transient Landforms And Interconnected Ecosystems.

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Predicting Instabilities In Transient Landforms And Interconnected Ecosystems.

Related Experiment Video

Simulating Impacts of Ice Storms on Forest Ecosystems

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Published on: June 30, 2020

Predicting instabilities in transient landforms and interconnected ecosystems.

Taylor Smith¹, Andreas Morr^2,3, Bodo Bookhagen⁴

¹Institute of Geosciences, Universität Potsdam, Potsdam, Germany. tasmith@uni-potsdam.de.

Nature Communications

|February 6, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces Floquet Multipliers to assess Earth system stability without data pre-processing. This method predicts catastrophic shifts in systems like glaciers and rainforests, improving climate and ecosystem stability analysis.

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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.