Dear list members in both hemispheres;
For my PhD thesis, I am studying the spatial variability of penetration
resistance (a proxy for strength) in snow layers in an Alpine snow
cover. Of specific interest are weak layers that are responsible for
snow avalanche release. Are such weak layers less spatially variable
than layers that are not critical for snow stability? To answer this, I
want to compare the range, sill and nugget from model semivariograms
calculated for all layers investigated. I also calculate mean and CV for
At 113 locations on each small slope (20m x 20m), measurements of
penetration resistance were made in a nested grid with a spacing of 0.5m
to 2m. The penetration resistance for each layer within the grid was
recorded at all locations. I have data from approximately 100 layers.
Weak layers were identified with separate tests within each grid.
The penetration resistance for each layer was log10 transformed to
approach normality. Grid-scale trends for each layer were investigated
with a (robust) linear regression on the x-y coordinates. In most
layers, this trend was statistically significant, but often in different
directions even in adjacent layers. The trend was removed to do a
geostatistical analysis on the normally distributed residuals. A robust
experimental semivariogram was calculated for the residuals for each
layer. Now I want to fit a spherical model semivariogram to the
experimental semivariograms. The spherical model fits the data from most
layers better than other models.
- Is it possible to compare directly the range, the sill and the nugget
of the spherical model semivariograms fitted to the residuals of the
linearly detrended data for each layer?
- Are there any pit-falls that I should be aware of? (Should I test
different semivariogram models for each layer? Should I leave the linear
trend in the data for the structural analyses? ...)
All comments, suggestions and references are welcome and will be much
appreciated. I will be happy to provide more info if needed.
PS: No one was hurt during the measurements ;-)
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