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(Article of periodic en Anglais - 2011)

Document title

Predicting monthly precipitation with multivariate regression methods using geographic and topographic information

Authors(s) and Affiliation(s)

SUN R. (1) ; CHEN L. (1) ; FU B. (1) ;
(1) State Key Lab. of Urban and Regional Ecology, Research Center for Eco-environmental Sciences, CAS, Beijing, CHINE

Abstract

Multivariate regression models that integrate topographic and geographic information are developed to predict monthly precipitation in the Daqing Mountains of northern China. Five geographic and topographic factors, including longitude, latitude, elevation, slope, and aspect, are taken into account in the model development. The data are acquired from a 100 m resolution DEM of the national topographic databases. Measured precipitation data at 56 stations between 1955 and 1990 are used for model development, and a leave-one-out cross-validation method is used for model evaluation. The model explains most of the spatial variability in monthly precipitation, and can also quantify the relative importance of different geographic and topographic variables

Source

Article of periodic

published at : Physical geography / ISSN 0272-3646

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

Millesime : 2011, vol. 32, no3 [pp. 269-285]

Bibliographic references : 2 p.

Collation : 5 fig., 4 tabl.

Language

Anglais

INIST-CNRS, Cote INIST : 20106

Digital Object Identifier

Go to electronic document thanks to its DOI : doi:10.2747/0272-3646.32.3.269

Tous droits réservés © Prodig - Bibliographie Géographique Internationale (BGI), 2012
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