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

Auteur(s) et Affiliation(s)



Description :
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.


Type de document :
Article de périodique

Source :
Physical geography, issn : 0272-3646, 2011, vol. 32, n°. 3, p. 269-285, nombre de pages : 17, Références bibliographiques : 2 p.

Date :
2011

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