nixmass: SWE modeling with the delta.snow process based model and...

In nixmass: Snow Water Equivalent Modeling with the 'Delta.snow' and 'HS2SWE' Models and Empirical Regression Models

SWE modeling with the delta.snow process based model and several empirical regression models.

Description

Snow Water Equivalent (SWE) is modeled either exclusively from daily snow depth changes or statistically, depending on snow depth, elevation, date and climate class.

Usage

nixmass(
  data,
  model = c("delta.snow", "delta.snow.dyn_rho_max", "hs2swe", "jo09", "pi16", "st10",
    "gu19"),
  alt,
  region.jo09,
  region.gu19,
  snowclass.st10,
  layers = FALSE,
  strict_mode = TRUE,
  verbose = FALSE
)

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).
model	Defines model for SWE computation. Can be one, several or all of 'delta.snow', 'delta.snow.dyn_rho_max', 'hs2swe', 'jo09', 'pi16', 'st10', 'gu19'. If no model is given, 'delta.snow' will be taken.
alt	Must be given in meter if one of model is 'jo09'. Ignored otherwise.
region.jo09	Must be given if one of model is 'jo09', ignored otherwise. This must be an integer number between 1 and 7 of the Swiss region where the station belongs to, according to Fig. 1 in the original reference.
region.gu19	If model contains 'gu19' this must be one of 'italy', 'southwest', 'central' or 'southeast' as described in the original reference. #' Ignored if model is not 'gu19'.
snowclass.st10	Must be given if one of model is 'st10'. Must be one of the following character strings: 'alpine', 'maritime', 'prairie', 'tundra', 'taiga' as outlined in the original reference. Ignored if model is not 'st10'.
layers	Logical. Should parameters snow depth, swe and age be returned layerwise?.
strict_mode	Logical. If 'TRUE', the function checks if the data is strictly regular and if the snow depth series starts with zero.
verbose	Logical. Should additional information be given during runtime?

Details

This function is a wrapper for the simulation of SWE with different models. The process based model delta.snow can be chosen in its original formulation (Winkler et al. 20219) and with a dynamically increasing maximum bulk snow density (Schroeder et al., 2024). The hs2swe model is an alternative formulation of the same physical concept used in delta.snow (Magnusson, et al., 2025). Some empirical regression models can also be chosen: Jonas,Pistocchi, Sturm and Guyennon. For the 'delta.snow' and 'hs2swe' models and the ones of 'Pistocchi' and 'Guyennon', the needed parameters and coefficients from the original references are set as default. They can however be changed according to results from other datasets. For the other models of 'Jonas' and 'Sturm' regression coefficients are fixed.

Computation is quite fast and there does not exist any restriction regarding the length of the data. However, if many years have to be modeled at once, it is recommended to split the computation into single years.

Value

A list of class nixmass with components:

swe	Contains a list of numerical vectors. Each entry refers to SWE values computed with the selected model(s).
date	Vector of date strings in the input class of format YYYY-MM-DD.
hs	Vector of given snow depth values used to compute SWE.

Author(s)

Harald Schellander, Michael Winkler

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.

Jonas, T., Marty, C. and Magnusson, J. (2009) "Estimating the snow water equivalent from snow depth measurements in the Swiss Alps"", Journal of Hydrology, 378(1 - 2), pp. 161 - 167. doi: 10.1016/j.jhydrol.2009.09.021.

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.

Sturm, M. et al. (2010) "Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes", Journal of Hydrometeorology, 11(6), pp. 1380 - 1394. doi: 10.1175/2010JHM1202.1.

Winkler, M., Schellander, H., and Gruber, S.: Snow water equivalents exclusively from snow depths and their temporal changes: the delta.snow model, Hydrol. Earth Syst. Sci., 25, 1165-1187, doi: 10.5194/hess-25-1165-2021, 2021.

Schroeder, M.et al. (2024) "CONTINUOUS SNOW WATER EQUIVALENT MONITORING ON GLACIERS USING COSMIC RAY NEUTRON SENSOR TECHNOLOGY A CASE STUDY ON HINTEREISFERNER, AUSTRIA", Proceedings: International Snow Science Workshop 2024, Tromsø, Norway, 1107 - 1114

Examples

# Load example data with realistic snow depth values
# from a station at 600 meters in the northern Alps
# Note that the winter season is set to an arbitrary date
# to mask its origin
data("hsdata")
o <- nixmass(hsdata, model="delta.snow",verbose=TRUE)
plot(o)

o1 <- nixmass(hsdata, alt=600, region.jo09=6, region.gu19 = "central",
              snowclass.st10 = "alpine", verbose = FALSE)
plot(o1)
summary(o1)

swe <- nixmass(hsdata, alt = 1000, region.jo09=1, snowclass.st10 = "tundra", region.gu19 = "italy")
summary(swe)

nixmass documentation built on June 8, 2025, 1:44 p.m.