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Watershed Planning within a Quantitative Scenario Analysis Framework

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Published on: July 24, 2016

Estimating Ecosystem Resilience From Noisy Observational Data.

Mengyang Cai¹, Yao Zhang¹, Jinghao Qiu¹

¹Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.

Global Change Biology

|July 22, 2025

Summary

Measurement noise can lead to underestimating ecosystem resilience using temporal autocorrelation (TAC). Higher temporal resolution and disturbance intensity improve TAC accuracy, crucial for reliable early warning signals of ecosystem tipping points.

Keywords:

disturbance noise resilience temporal autocorrelation tipping point

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Area of Science:

Ecology
Environmental Science
Data Science

Background:

Ecosystem resilience, the capacity to recover from disturbances, is vital for persistence under global change.
Temporal autocorrelation (TAC) of ecosystem states is a key indicator of declining resilience and proximity to tipping points.
The impact of measurement noise on TAC-based resilience assessments is not well understood.

Purpose of the Study:

To investigate how measurement noise affects the calculation of temporal autocorrelation (TAC) for ecosystem resilience.
To determine the extent to which noise influences the accuracy of resilience decline detection.
To provide guidance for more robust resilience assessments in the face of noisy data.

Main Methods:

Mathematical derivations to theoretically assess noise effects on TAC.
Idealized simulation experiments with controlled noise levels.
Analysis of remote sensing datasets with varying degrees of measurement noise.

Main Results:

TAC estimates are systematically lower with increasing measurement noise.
The degree of underestimation depends on noise levels, observational frequency, and disturbance intensity.
Higher temporal resolution and greater disturbance intensity improve TAC accuracy under noise.

Conclusions:

Measurement noise can bias resilience trends and generate false early warning signals.
Increasing observational temporal resolution and employing advanced data processing can mitigate noise impacts.
Accurate assessment of global ecosystem resilience requires careful consideration and management of measurement noise.