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(Article of periodic en Anglais - 2013)

Document title

Incorporation of multi-scale spatial autocorrelation in soil moisture–landscape modeling

Authors(s) and Affiliation(s)

KIM D. (1) ;
(1) Dept. of Geography, Biogeomorphology Research and Analysis Group, Univ. of Kentucky, Lexington, ETATS-UNIS

Abstract

Based on soil, vegetation, and topographic data collected in the Sindu coastal dunefield in western Korea, this research developed 3 soil moisture–landscape models, each incorporating spatial autocorrelation (SAC) at fine, broad, and multiple scales, respectively, into a non-spatial ordinary least squares (OLS) model. All of these spatially explicit models showed better performance than the OLS model. In particular, the best model was proved to be the one using spatial eigenvector mapping, a technique that accounts for spatial structure at multiple scales simultaneously. It is highlighted that the conventional regression modeling may have a reduced predictive power in reality, in cases where they possess a significant amount of SAC. This research demonstrates that accounting for spatial structure allows a better understanding of dynamic soil hydrological processes occurring at different spatial scales

Source

Article of periodic

published at : Physical geography / ISSN 0272-3646

Editor : Taylor & Francis, Abingdon - ROYAUME-UNI (1980)

Millesime : 2013, vol. 34, no6 [pp. 441-455]

Bibliographic references : 39 ref.

Collation : 3 fig., 2 tabl.

Language

Anglais

INIST-CNRS, Cote INIST : 20106

Digital Object Identifier

Go to electronic document thanks to its DOI : doi:10.1080/02723646.2013.857267

Tous droits réservés © Prodig - Bibliographie Géographique Internationale (BGI), 2014
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