Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 501863
Title Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks
Author(s) Papale, Dario; Black, T.A.; Carvalhais, Nuno; Cescatti, Alessandro; Chen, Jiquan; Jung, Martin; Kiely, Gerard; Lasslop, Gitta; Mahecha, Miguel D.; Margolis, Hank; Merbold, Lutz; Montagnani, Leonardo; Moors, Eddy; Olesen, J.E.; Reichstein, Markus; Tramontana, Gianluca; Gorsel, Eva Van; Wohlfahrt, Georg; Ráduly, Botond
Source Journal of Geophysical Research: Biogeosciences 120 (2015)10. - ISSN 2169-8953 - p. 1941 - 1957.
DOI https://doi.org/10.1002/2015JG002997
Department(s) Alterra - Climate change and adaptive land and water management
Publication type Refereed Article in a scientific journal
Publication year 2015
Keyword(s) artificial neural networks - gross primary production - latent heat - representativeness - uncertainty - upscaling
Abstract

Empirical modeling 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 d-1). Extrapolation in space in similar climatic and vegetation conditions also gave good results (GPP MAE 0.7-1.41 gC m-2 d-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 d-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. Key Points Uncertainty due to spatial sampling is evaluated using ANNs and FLUXNET data GPP and LE budgets and IAV are analyzed with different site networks The uncertainty in upscaling due to spatial sampling is highly heterogeneous

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