Patching rainfall data using regression methods. 3. Grouping, patching and outlier detection

Rainfall data are used, amongst other things, for augmenting or repairing streamflow records in a water resources analysis environment. Gaps in rainfall records cause problems in the construction of water-balance models using monthly time-steps, when it becomes necessary to estimate missing values....

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Veröffentlicht in:Journal of hydrology (Amsterdam) Jg. 198; H. 1; S. 319 - 334
1. Verfasser: Pegram, Geoffrey
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.11.1997
Schlagworte:
algorithms
covariance
linear models
meteorological data
rain
regression analysis
stream flow
water resources
ISSN:0022-1694, 1879-2707
Online-Zugang:Volltext
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Zusammenfassung:Rainfall data are used, amongst other things, for augmenting or repairing streamflow records in a water resources analysis environment. Gaps in rainfall records cause problems in the construction of water-balance models using monthly time-steps, when it becomes necessary to estimate missing values. Modest extensions are sometimes also desirable. It is also important to identify outliers as possible erroneous data and to group data which are hydrologically similar in order to accomplish good patching. Algorithms are described which accomplish these tasks using the covariance biplot, multiple linear regression, singular value decomposition and the pseudo-Expectation-Maximization algorithm.
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ISSN:0022-1694
1879-2707
DOI:10.1016/S0022-1694(96)03284-2