DSpace

Digital.CSIC > Recursos Naturales > Instituto Pirenaico de Ecología (IPE) > (IPE) Artículos >

Share

EndNote

Impact

Links

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

Authors:López-Moreno, Juan I.
Latrón, J.
Lehmann, Anthony
Keywords:Regression-based methods, Spatial interpolation, Sample size, DEM resolution, Snow
Issue Date:1-Jul-2010
Publisher:Wiley-Blackwell
Citation:Hydrological Processes 24(14): 1914-1928 (2010)
Abstract: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 km2) 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.
Description:15 páginas, 11 figuras, 1 tabla.
Publisher version (URL):http://dx.doi.org/10.1002/hyp.7564
URI:http://hdl.handle.net/10261/35066
ISSN:0885-6087
???metadata.dc.identifier.doi???:10.1002/hyp.7564
Appears in Collections:(IPE) Artículos

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.