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Updated: May 30, 2026
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Uncertainty in predictions of forest carbon dynamics: separating driver error from model error.
L Spadavecchia1, M Williams, B E Law
1School of GeoSciences and NERC Centre for Terrestrial Carbon Dynamics, University of Edinburgh, Edinburgh EH9 3JN, United Kingdom.
Summary
Model parameter uncertainty significantly impacts carbon flux estimates more than driver uncertainty. Improving meteorological data proximity is crucial for accurate ecological forecasting and reducing prediction bias.
Area of Science:
- Ecology
- Environmental Science
- Climate Science
Background:
- Estimating net carbon fluxes is vital for understanding ecosystem dynamics and climate change.
- Uncertainty in model parameters and driving data (e.g., meteorological variables) complicates flux estimations.
- Data gaps in observational networks and sensor failures contribute to driver uncertainty.
Purpose of the Study:
- To analyze the relative contribution of parameter and driver uncertainty to carbon flux estimates.
- To partition model uncertainty between temperature and precipitation drivers.
- To assess the impact of meteorological data proximity on prediction uncertainty.
Main Methods:
- Utilized data from a ponderosa pine stand in Central Oregon and the DALEC model.
- Generated parameter uncertainty using an ensemble Kalman filter and eddy covariance data.
- Created meteorological driver ensembles via geostatistical simulations from local weather station data.
Main Results:
- Parameter uncertainty (50% of total net flux) exceeded driver uncertainty (10%) when stations were nearby (<25 km).
- Temperature was the largest source of driver uncertainty (8%).
- Increased distance to meteorological stations (>100 km) amplified NEE prediction uncertainty by 88%.
Conclusions:
- Parameterization uncertainty is a dominant factor in carbon flux estimation.
- Meteorological driver uncertainty significantly increases with distance from observational stations.
- The developed approach aids in assessing meteorological driver bias for regional ecological forecasting.
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