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Evaluating Ecohydrological Model Sensitivity to Input Variability with an Information-Theory-Based Approach.

Mozhgan A Farahani1, Alireza Vahid2, Allison E Goodwell1

  • 1Department of Civil Engineering, University of Colorado Denver, Denver, CO 80204, USA.

Entropy (Basel, Switzerland)
|July 27, 2022
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Summary

Quantizing ecohydrological model forcing data impacts heat and carbon fluxes. The study introduces a method to simplify time-series data without significantly reducing model performance, aiding sensitivity analysis.

Keywords:
ecohydrological modelinginformation theoryquantizationrate-distortion theorysensitivity analysis

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Area of Science:

  • Earth and Environmental Sciences
  • Computational Science
  • Ecology

Background:

  • Ecohydrological models' sensitivity to forcing data varies.
  • Understanding how models utilize input information is crucial for accurate simulations.

Purpose of the Study:

  • To investigate the impact of forcing data precision on ecohydrological model behavior.
  • To develop a method for optimally simplifying time-series forcing data using rate-distortion theory.

Main Methods:

  • Quantizing (binning) time-series forcing variables (shortwave radiation, air temperature, vapor pressure deficit, wind speed) using rate-distortion theory.
  • Evaluating the effects of different quantization levels on simulated heat and carbon fluxes using a multi-layer canopy model.
  • Validating model outputs with eddy covariance flux tower data.

Main Results:

  • The model demonstrated higher sensitivity to quantized shortwave radiation compared to other meteorological forcing inputs.
  • Model responses to input quantization varied with seasonal conditions and input combinations.
  • Carbon flux simulations were similarly affected by any level of quantization, while heat fluxes showed varying sensitivity to specific quantization levels.

Conclusions:

  • Optimal simplification of forcing time series is achievable, often without substantial loss of model performance.
  • The developed method can enhance sensitivity analyses, providing insights into how ecohydrological models process available information.