Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67179843%3A_____%2F15%3A00451409" target="_blank" >RIV/67179843:_____/15:00451409 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1002/2015JG002997" target="_blank" >http://dx.doi.org/10.1002/2015JG002997</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/2015JG002997" target="_blank" >10.1002/2015JG002997</a>

Result language
angličtina
Original language name
Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks
Original language description
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?m2?d1). Extrapolation in space in similar climatic and vegetation conditions also gave good results (GPP MAE 0.7?1.41?gC?m2?d1), while extrapolation in areas with different seasonal cycles
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
EH - Ecology - communities
OECD FORD branch
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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