SWE modeling with the delta.snow process based model and several empirical regression models.
nixmass.RdSnow 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
dateandhs. 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 formatYYYY-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?
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.
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.
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)
#> Using parameters:
#> rho.max rho.null c.ov k.ov k tau
#> 4.012588e+02 8.119417e+01 5.104722e-04 3.785674e-01 2.993175e-02 2.362476e-02
#> eta.null timestep
#> 8.523356e+06 2.400000e+01
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plot(o)
o1 <- nixmass(hsdata, alt=600, region.jo09=6, region.gu19 = "central",
snowclass.st10 = "alpine", verbose = FALSE)
plot(o1)
summary(o1)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.00000 0.00000 0.00000 38.58482 28.34948 282.25331
swe <- nixmass(hsdata, alt = 1000, region.jo09=1, snowclass.st10 = "tundra", region.gu19 = "italy")
summary(swe)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.00000 0.00000 0.00000 38.58482 28.34948 282.25331