Estimating the spatial distribution of snow water equivalent in an alpine basin using binary regression tree models: the impact of digital elevation data and independent variable selection

Regression tree models have been shown to provide the most accurate estimates of distributed snow water equivalent (SWE) when intensive field observations are available. This work presents a comparison of regression tree models using different source digital elevation models (DEMs) and different combinations of independent variables. Different residual interpolation techniques are also compared. The analysis was performed in the 19·1 km2 Tokopah Basin, located in the southern Sierra Nevada of California. Snow depth, the dependent variable of the statistical models, was derived from three snow surveys (April, May and June 1997), with an average of 328 depth measurements per survey. Estimates of distributed SWE were derived from the product of the snow depth surfaces, the average snow density (54 measurements on average) and the fractional snow covered area (obtained from the Landsat Thematic Mapper and the Airborne Visible/Infrared Imaging Spectrometer). Independent variables derived from the standard US Geological Survey DEM yielded the lowest overall model deviance and lowest error in snow depth prediction. Simulations using the Shuttle Radar Topography Mission DEM and the National Elevation Dataset DEM were improved when northness was substituted for solar radiation in five of six cases. Co‐kriging with maximum upwind slope and elevation proved to be the best method for distributing residuals for April and June, respectively. Inverse distance weighting was the best residual distribution method for May. Copyright © 2004 John Wiley & Sons, Ltd.

[1]  R. Lyman Ott.,et al.  An introduction to statistical methods and data analysis , 1977 .

[2]  R. Colwell.  Remote sensing of the environment , 1980, Nature.

[3]  Jeff Dozier,et al.  A clear‐sky spectral solar radiation model for snow‐covered mountainous terrain , 1980 .

[4]  R. Bilonick.  An Introduction to Applied Geostatistics , 1989 .

[5]  Jürg Hosang,et al.  Evaluation of a water equivalent of snow cover map in a small catchment area using a geostatistical approach , 1991 .

[6]  Michael Edward Hohn,et al.  An Introduction to Applied Geostatistics: by Edward H. Isaaks and R. Mohan Srivastava, 1989, Oxford University Press, New York, 561 p., ISBN 0-19-505012-6, ISBN 0-19-505013-4 (paperback), $55.00 cloth, $35.00 paper (US) , 1991 .

[7]  Trevor Hastie,et al.  Statistical Models in S , 1991 .

[8]  K. Tonnessen.  The Emerald Lake Watershed Study: Introduction and site description , 1991 .

[9]  K. Tonnessen,et al.  Biogeochemistry of seasonally snow-covered catchments , 1995 .

[10]  Noel A Cressie,et al.  A comparison of geostatistical methodologies used to estimate snow water equivalent , 1996 .

[11]  Jeff Dozier,et al.  Automated Mapping of Montane Snow Cover at Subpixel Resolution from the Landsat Thematic Mapper , 1996 .

[12]  Kelly Elder,et al.  Estimating the spatial distribution of snow water equivalence in a montane watershed , 1998 .

[13]  Stephen P. Kaluzny,et al.  S+SpatialStats: User’s Manual for Windows® and UNIX® , 1998 .

[14]  Jeff Dozier,et al.  Estimating the spatial distribution of snow in mountain basins using remote sensing and energy balance modeling , 1998 .

[15]  Kelly Elder,et al.  Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed , 2000 .

[16]  Kelly Elder,et al.  Spatial Snow Modeling of Wind-Redistributed Snow Using Terrain-Based Parameters , 2002 .

[17]  Kelly Elder,et al.  Comparison of spatial interpolation methods for estimating snow distribution in the Colorado Rocky Mountains , 2002 .

[18]  T. Painter,et al.  Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data , 2003 .

[19]  E. Schanda,et al.  Remote sensing of the environment , 1978, Naturwissenschaften.