Effects of sample and grid size on the accuracy and stability of regression-based snow interpolation methods

This work analyses the responses of four regression-based interpolation methods for predicting snowpack distribution to changes in the number of data points (sample size) and resolution of the employed digital elevation model (DEM). For this purpose, we used data obtained from intensive and random sampling of snow depth (991 measurements) in a small catchment (6 km(2)) in the Pyrenees, Spain. Linear regression, classification trees, generalized additive models (GAMs), and a recent method based on a correction made by applying tree classification to GAM residuals were used to calculate snow-depth distribution based on terrain characteristics under different combinations of sample size and DEM spatial resolution (grid size). The application of a tree classification to GAM residuals yielded the highest accuracy scores and the most stable models. The other tested methods yielded scores with slightly lower accuracy and varying levels of robustness under different conditions of grid and sample size. The accuracy of the model predictions declined with decreasing resolution of DEMs and sample size; however, the sensitivities of the models to the number of data points showed threshold values, which has implications (when planning fieldwork) for optimizing the relation between the effort expended in gathering data and the quality of the results. Copyright (C) 2009 John Wiley & Sons, Ltd.