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| Titel |
State-space approach to evaluate spatial variability of field measured soil water status along a line transect in a volcanic-vesuvian soil |
| VerfasserIn |
A. Comegna, A. Coppola, V. Comegna, G. Severino, A. Sommella, C. D. Vitale |
| 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 ; 14, no. 12 ; Nr. 14, no. 12 (2010-12-07), S.2455-2463 |
| Datensatznummer |
250012524
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| Publikation (Nr.) |
 copernicus.org/hess-14-2455-2010.pdf |
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| Zusammenfassung |
| Unsaturated hydraulic properties and their spatial variability today are
analyzed in order to use properly mathematical models developed to simulate
flow of the water and solute movement at the field-scale soils. Many studies
have shown that observations of soil hydraulic properties should not be
considered purely random, given that they possess a structure which may be
described by means of stochastic processes. The techniques used for
analyzing such a structure have essentially been based either on the theory
of regionalized variables or to a lesser extent, on the analysis of time
series. This work attempts to use the time-series approach mentioned above
by means of a study of pressure head h and water content θ which
characterize soil water status, in the space-time domain. The data of the
analyses were recorded in the open field during a controlled drainage
process, evaporation being prevented, along a 50 m transect in a volcanic
Vesuvian soil. The isotropic hypothesis is empirical proved and then the
autocorrelation ACF and the partial autocorrelation functions PACF were used
to identify and estimate the ARMA(1,1) statistical model for the analyzed
series and the AR(1) for the extracted signal. Relations with a state-space
model are investigated, and a bivariate AR(1) model fitted. The simultaneous
relations between θ and h are considered and estimated. The results
are of value for sampling strategies and they should incite to a larger use
of time and space series analysis. |
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