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Free keywords:
Earth Observations; Essential Ecosystem Variables
Abstract:
Empirical modelling approaches are frequently used to upscale local eddy-covariance observations of
carbon, water and energy fluxes to regional and global scales. The predictive capacity of such models largely
depends on the data used for parameterization and identification of input-output relationships, while
prediction for conditions outside the training domain is generally uncertain. In this work, artificial neural
networks (ANNs) were used for the prediction of gross primary production (GPP) and latent heat flux (LE) on
local and European scales with the aim to assess the portion of uncertainties in extrapolation due to sample
selection. ANNs were found to be a useful tool for GPP and LE prediction, in particular for extrapolation in
time (mean absolute error MAE for GPP between 0.53 and 1.56 gC m-2 day-1). Extrapolation in space in
similar climatic and vegetation conditions also gave good results (GPP MAE 0.7-1.41 gC m-2 day-1), while
extrapolation in areas with different seasonal cycles and controlling factors (e.g. the tropical regions)
showed noticeably higher errors (GPP MAE 0.8-2.09 gC m-2 day-1). The distribution and the number of sites
used for ANN training had a remarkable effect on prediction uncertainty in both, regional GPP and LE
budgets and their interannual variability. Results obtained show that for ANN upscaling for continents with
relatively small networks of sites, the error due to the sampling can be large and needs to be considered
and quantified. The analysis of the spatial variability of the uncertainty helped to identify the meteorological drivers driving the uncertainty.
