rsaga.wetness.index(in.dem, out.wetness.index, out.carea, out.cslope, out.mod.carea, suction, area.type, slope.type, slope.min, slope.offset, slope.weight, t.param, env = rsaga.env(), ...).sgrd)"absolute" (or numeric code 0): absolute catchment area; "square root" (code 1; the default): square root of catchment area; "specific" (code 2): specific catchment area"local" (or numeric code 0): local slope; "catchment" (or code 1; the default): catchment slope.rsaga.env.)rsaga.geoprocessorintern argument passed to the rsaga.geoprocessor. For intern=FALSE it is a numerical error code (0: success), or otherwise (the default) a character vector with the module's console output.
out.mod.carea), which does not treat the flow as a thin film as done in the calculation of catchment areas in conventional algorithms. As a result, the SWI tends to assign a more realistic, higher potential soil wetness than the TWI to grid cells situated in valley floors with a small vertical distance to a channel.This module and its arguments changed substantially from SAGA GIS 2.0.8 to version 2.1.0. It appears to me that the new algorithm is similar (but not identical) to the old one when using area.type="absolute" and slope.type="local" but I haven't tried out all possible options. This help file will be updated as soon as additional documentation becomes available.
Boehner, J. and Selige, T. (2006): Spatial prediction of soil attributes using terrain analysis and climate regionalisation. In: Boehner, J., McCloy, K.R., Strobl, J. [Ed.]: SAGA - Analysis and Modelling Applications, Goettinger Geographische Abhandlungen, Goettingen: 13-28.
rsaga.parallel.processing, rsaga.geoprocessor, rsaga.env
## Not run:
# # using SAGA grids:
# rsaga.wetness.index("dem.sgrd","swi.sgrd")
# ## End(Not run)
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