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Area of Science:

  • Ecohydrology
  • Atmospheric Science
  • Complex Systems Analysis

Background:

  • Biosphere-atmosphere interactions involve complex, nonlinear dynamics.
  • Understanding information flow is key to inferring causal dependencies in ecohydrological systems.

Purpose of the Study:

  • To quantify the evolutionary dynamics of the ecohydrological complex system using high-frequency data.
  • To investigate the relationship between structural and functional differences in information flow.
  • To test the hypothesis that causal dependencies drive system dynamics and functionality.

Main Methods:

  • Utilized high-frequency (10 Hz) multivariate time series data from eddy covariance measurements.
  • Applied directed acyclic graph (DAG) analysis to infer causal dependencies and information flow.
  • Characterized system functionality by analyzing redundant, unique, and synergistic components of information flow.

Main Results:

  • Identified distinct drivers for atmospheric/thermodynamic variables versus scalar transport (CO2, H2O).
  • Atmospheric and thermodynamic dynamics are primarily driven by non-local conditions.
  • Scalar transport of CO2 and H2O is mainly influenced by short-term local conditions.

Conclusions:

  • Causal dependencies, inferred through information flow, structure ecohydrological system dynamics.
  • Functional differences in information flow correspond to distinct driving conditions (local vs. non-local).
  • The study provides insights into the complex interplay governing the biosphere-atmosphere interface.