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| Titel |
Modeling moisture fluxes using artificial neural networks: can information extraction overcome data loss? |
| VerfasserIn |
A. L. Neal, H. V. Gupta, S. A. Kurc, P. D. Brooks |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 15, no. 1 ; Nr. 15, no. 1 (2011-01-26), S.359-368 |
| Datensatznummer |
250012608
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| Publikation (Nr.) |
 copernicus.org/hess-15-359-2011.pdf |
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| Zusammenfassung |
| Eddy covariance sites can experience data losses as high as 30 to 45% on
an annual basis. Artificial neural networks (ANNs) have been identified as
powerful tools for gap filling, but their performance depends on the
representativeness of data used to train the model. In this paper, we
develop a normalization method, which has similar performance compared to
conventional training approaches, but exhibits differences in the timing of
fluxes, indicating different and previously unused information in the data
record. Specifically, the differences between half-hourly model fluxes,
especially during summer months, indicate that the structure of the
information content in the data changes seasonally, diurnally and with the
rate of data loss. Extracting more information from data may not improve
model performance and indicates the need for improved data and models to
address flux behavior at critical times. We advise several approaches to
address these concerns, including use of separate models for day and
nighttime processes and the use of alternate data streams at dawn, when eddy
covariance may be particularly ineffective due to the timing of the onset of
turbulent mixing. |
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