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

Auteur(s) et Affiliation(s)

KIM, D.
Dept. of Geography, Biogeomorphology Research and Analysis Group, Univ. of Kentucky, Lexington, Etats-Unis


Description :
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.


Type de document :
Article de périodique

Source :
Physical geography, issn : 0272-3646, 2013, vol. 34, n°. 6, p. 441-455, nombre de pages : 15, Références bibliographiques : 39 ref.

Date :
2013

Editeur :
Pays édition : Royaume-Uni, Abingdon, Taylor and Francis

Langue :
Anglais
Droits :
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