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|Title:||Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks|
|Authors:||PAPALE DARIO; BLACK ANDREW; CARVALHAIS NUNO; CESCATTI ALESSANDRO; CHEN JIQUAN; JUNG MARTIN; KIELY GERALD; LASSLOP GITTA; MAHECHA M.D.; MARGOLIS HANK; MERBOLD LUTZ; MONTAGNANI LEONARDO; MOORS E.; OLESEN JORGEN E.; REICHSTEIN MARKUS; TRAMONTANA GIANLUCA; VAN GORSEL EVA; WOHLFAHRT GEORG; RADULY BOTOND|
|Citation:||JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES vol. 120 no. 10 p. 1941-1957|
|Publisher:||AMER GEOPHYSICAL UNION|
|Type:||Articles in periodicals and books|
|Abstract:||Empiricalmodeling 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 gCm2 d1). Extrapolation in space in similar climatic and vegetation conditions also gave good results (GPP MAE 0.7–1.41 gCm2 d1), 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 gCm2 d1). 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.|
|JRC Directorate:||Space, Security and Migration|
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