Statistical SWE modeling based on a quadratic dependance on the day-of-year

This model parameterizes bulk snow density with day-of-the-year as the only input similar to swe.pi16 but adds a quadratic dependance. It was calibrated for the regions of the whole Italian alps, and the subregions South-West, Central and South-East. By setting the coefficients of the empirical regression it can however be used with results from other datasets.

Usage

swe.gu19(data, region.gu19, n0 = NA, n1 = NA, n2 = NA)

Arguments

data: A data.frame of daily observations with two columns named date and hs referring to day and snow depth at that day. The date column can be of class `character`, `Date` or `POSIXct` with the format YYYY-MM-DD. The hs column must be snow depth values \(\ge 0\) in m.
region.gu19: Must be one of the italian subalpine regions italy, southwest, central or southeast, defined in the original reference (see details), or myregion, in which case the coefficients n0, n1 and n2 have to be set.
n0: Intercept of an empirical regression between densities and the day-of-year (see details).
n1: Slope of an empirical regression between densities and the day-of-year (see details).
n2: Quadratic dependence of an empirical regression between densities and the day-of-year (see details).

Value

A vector with daily SWE values in mm.

Details

swe.gu19 Similar to the model of Pistocchi (2016), this function uses only the day-of-year (DOY) as parameterization for bulk snow density and hence SWE. In contrast to the latter, here, a quadratic term for DOY was added, to reflect non-linearity in the snow bulk density variability. The datums in the input data.frame are converted to DOY as days spent since November 1st. Regression coefficients depend on regions defined in Guyennon et al. (2019), which are italy for the Italian Alps, southwest for the South-western Italian Alps, central for the Central Italian Alpes or southeast for the South-western Italian Alps.

If region.gu19 is set to myregion, the coefficients no, n1 and n2 must be set to values, obtained from a regression between densities and day-of-year from another dataset. It has to have the form density ~ DOY + DOY^2, where DOY is the day-of-year as defined in the original reference. Non computable values are returned as NA.

References

Guyennon, N., Valt, M., Salerno, F., Petrangeli, A., Romano, E. (2019) 'Estimating the snow water equivalent from snow depth measurements in the Italian Alps', Cold Regions Science and Technology. Elsevier, 167 (August), p. 102859. doi: 10.1016/j.coldregions.2019.102859.

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.gu19(hsdata, region = "italy")
summary(swe)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#>   0.000   0.000   5.488  58.126  53.262 391.731      92