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Statistical SWE modeling depending on the day-of-year

swe.pi16.Rd

This model parameterizes bulk snow density with day-of-the-year as the only input. It was calibrated for the region of South Tyrol, Italy, and is therefore called ST model in the original reference.

Usage

swe.pi16(data, rho_0 = 200, K = 1)

Arguments

data

A data.frame with at least two columns named date and hs. They should contain date and corresponding daily observations of snow depth \(hs \ge 0\) measured at one site. The unit must be meters (m). No gaps or NA are allowed. Dates must be either of class `character`, `Date` or `POSIXct` and given in the format YYYY-MM-DD. No sub-daily resolution is allowed at the moment (see details).

rho_0

Intercept of the linear regression between observed snow depths and SWE values. rho_0 is set to 200 as default, which is the value from the original reference. It can however be set to any value according to regression modeling with other datasets.

K

Slope of the linear regression between observed densities and the day-of-year as defined in the original reference. K is set to 1 as default, which is the value from the original reference. It can however be set to any value according to regression modeling with other datasets.

Value

A vector with daily SWE values in mm.

Details

swe.pi16 This function uses only the day-of-year (DOY) as parameterization for bulk snow density and hence SWE. Here, the datums in the input data.frame are converted to DOY as defined in the original reference: negative values between 1.10. and 31.12. DOY=-92 at 1.10. In leap years 31.12. has DOY = 0, in non-leap years 31.12. has DOY = -1 with no day being 0. Non computable values are returned as NA.

References

Pistocchi, A. (2016) 'Simple estimation of snow density in an Alpine region', Journal of Hydrology: Regional Studies. Elsevier B.V., 6(Supplement C), pp. 82 - 89. doi: 10.1016/j.ejrh.2016.03.004.

Examples

data(hsdata)
swe <- swe.pi16(hsdata)
summary(swe)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    0.00    0.00    0.00   44.71   32.64  410.80