Evaluating Ecohydrological Model Sensitivity to Input Variability with an Information-Theory-Based Approach
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Department of Civil Engineering, University of Colorado Denver, Denver, CO 80204, USA
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Department of Electrical Engineering, University of Colorado Denver, Denver, CO 80204, USA
*
Author to whom correspondence should be addressed.
†
Current address: Department of Civil Engineering, University of Colorado Denver, P.O. Box 173364, Denver, CO 80217, USA.
Academic Editor: Luciano Telesca
Entropy 2022, 24(7), 994; https://doi.org/10.3390/e24070994
Received: 17 June 2022
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Revised: 16 July 2022
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Accepted: 16 July 2022
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Published: 18 July 2022
(This article belongs to the Special Issue Complexity and Nonlinear Dynamics in the Geosciences: Methods and Applications)
Ecohydrological models vary in their sensitivity to forcing data and use available information to different extents. We focus on the impact of forcing precision on ecohydrological model behavior particularly by quantizing, or binning, time-series forcing variables. We use rate-distortion theory to quantize time-series forcing variables to different precisions. We evaluate the effect of different combinations of quantized shortwave radiation, air temperature, vapor pressure deficit, and wind speed on simulated heat and carbon fluxes for a multi-layer canopy model, which is forced and validated with eddy covariance flux tower observation data. We find that the model is more sensitive to radiation than meteorological forcing input, but model responses also vary with seasonal conditions and different combinations of quantized inputs. While any level of quantization impacts carbon flux similarly, specific levels of quantization influence heat fluxes to different degrees. This study introduces a method to optimally simplify forcing time series, often without significantly decreasing model performance, and could be applied within a sensitivity analysis framework to better understand how models use available information.
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Keywords:
sensitivity analysis; quantization; information theory; rate-distortion theory; ecohydrological modeling
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MDPI and ACS Style
Farahani, M.A.; Vahid, A.; Goodwell, A.E. Evaluating Ecohydrological Model Sensitivity to Input Variability with an Information-Theory-Based Approach. Entropy 2022, 24, 994. https://doi.org/10.3390/e24070994
AMA Style
Farahani MA, Vahid A, Goodwell AE. Evaluating Ecohydrological Model Sensitivity to Input Variability with an Information-Theory-Based Approach. Entropy. 2022; 24(7):994. https://doi.org/10.3390/e24070994
Chicago/Turabian StyleFarahani, Mozhgan A., Alireza Vahid, and Allison E. Goodwell. 2022. "Evaluating Ecohydrological Model Sensitivity to Input Variability with an Information-Theory-Based Approach" Entropy 24, no. 7: 994. https://doi.org/10.3390/e24070994
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