Interpolating local snow depth data: an evaluation of methods

Abstract

Snow depth measurements have been taken since 1986 at 106 snow poles distributed in the Spanish Pyrenees. Here, we compared the capacity of several local, geostatistical and global interpolator methods for mapping the spatial distribution of averaged snowpack (1986–2000) and the snowpack distribution in two single years with different climatic conditions. The error estimators indicate that the terrain complexity of the area makes it difficult to apply local and geostatistical methods satisfactorily. Regression-tree models provide an accurate description of the data set used (the calibration phase), but they show a relatively low predictive capability for the study case (the validation phase). Using linear regression and generalized additive models (GAMs), we achieved more robust estimations than by means of a regression-tree model. The GAMs give the most accurate prediction because they consider the non-linear relationships between snowpack and the external characteristics (physical features) of the sampling points.