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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

EH - Ekologie – společenstva

OECD FORD obor

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Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Geophysical Research : Biogeosciences

ISSN

2169-8953

e-ISSN

—

Svazek periodika

120

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

1941-1957

Kód UT WoS článku

000368730300005

EID výsledku v databázi Scopus

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