nixmass {nixmass}

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

Description

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

Usage

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

Arguments

data

A data.frame of daily observations with two columns named date and hs referring to day and snow depth at that day. Values in the date column must be of class character with format YYYY-MM-DD. Values in the hs column must be snow depth values of class numeric \ge 0 in m. No gaps or NA are allowed.

model

Defines model for SWE computation. Can be one, several or all of "delta.snow","jo09","pi16","st10","gu19". If no model is given, all models are computed.

alt

Must be given if one of model is "jo09". Station elevation in meters

region.jo09

Must be given if one of model is "jo09". 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.

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.

verbose

Logical. Should additional information be given during runtime?

Details

nixmass This function is a wrapper for the computation of SWE with different models. The process based model delta.snow can be chosen, as well as different empirical regression models of Jonas,Pistocchi, Sturm and Guyennon. For the "delta.snow" model 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.

The 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, separated by zero snow depth values.

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

Character vector of date strings in the format YYYY-MM-DD.

hs

Vector of 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.

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)