Uncertainty in predictions of forest carbon dynamics: separating driver error from model error

L. Spadavecchia, M. Williams, B. E. Law

Research output: Contribution to journalArticlepeer-review

Abstract

We present an analysis of the relative magnitude and contribution of parameter and driver uncertainty to the confidence intervals on estimates of net carbon fluxes. Model parameters may be difficult or impractical to measure, while driver fields are rarely complete, with data gaps due to sensor failure and sparse observational networks. Parameters are generally derived through some optimization method, while driver fields may be interpolated from available data sources. For this study, we used data from a young ponderosa pine stand at Metolius, Central Oregon, and a simple daily model of coupled carbon and water fluxes (DALEC). An ensemble of acceptable parameterizations was generated using an ensemble Kalman filter and eddy covariance measurements of net C exchange. Geostatistical simulations generated an ensemble of meteorological driving variables for the site, consistent with the spatiotemporal autocorrelations inherent in the observational data from 13 local weather stations. Simulated meteorological data were propagated through the model to derive the uncertainty on the CO2 flux resultant from driver uncertainty typical of spatially extensive modeling studies. Furthermore, the model uncertainty was partitioned between temperature and precipitation. With at least one meteorological station within 25 km of the study site, driver uncertainty was relatively small (similar to 10% of the total net flux), while parameterization uncertainty was larger, similar to 50% of the total net flux. The largest source of driver uncertainty was due to temperature (8% of the total flux). The combined effect of parameter and driver uncertainty was 57% of the total net flux. However, when the nearest meteorological station was >100 km from the study site, uncertainty in net ecosystem exchange (NEE) predictions introduced by meteorological drivers increased by 88%. Precipitation estimates were a larger source of bias in NEE estimates than were temperature estimates, although the biases partly compensated for each other. The time scales on which precipitation errors occurred in the simulations were shorter than the temporal scales over which drought developed in the model, so drought events were reasonably simulated. The approach outlined here provides a means to assess the uncertainty and bias introduced by meteorological drivers in regional-scale ecological forecasting.

Original languageEnglish
Pages (from-to)1506-1522
Number of pages17
JournalEcological Applications
Volume21
Issue number5
DOIs
Publication statusPublished - Jul 2011

Keywords

  • carbon dynamics
  • data assimilation
  • ensemble Kalman filter
  • geostatistics
  • product-sum covariance model
  • process-based modeling
  • PONDEROSA PINE FORESTS
  • NET PRIMARY PRODUCTION
  • BAYESIAN CALIBRATION
  • MOUNTAINOUS TERRAIN
  • DATA ASSIMILATION
  • ECOSYSTEM MODEL
  • SOIL SCIENCE
  • WATER
  • OREGON
  • FLUX

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