Introduction to the sarp.snowprofile package

Functions to import/export various formats of snow profiles, with basic formatting and visualization functions.

library(sarp.snowprofile)

1. Snowprofile objects

The package uses S3 classes for individual snow profiles (class snowprofile) or lists of multiple snow profiles (class snowprofileSet). Objects with these classes can be created, manipulated, and visualized.

A snowprofile object contains data about a snow stratigraphy profile. It is structured as a list with metadata (e.g. profile name and location) and a data.frame containing layer properties. Mandatory parts of a snowprofile object include:

A snowprofileSet object is simply a list of multiple snowprofile objects.

The package includes sample data packaged into three snowprofileSet objects:

## Print the structure of a single `snowprofile` object
Profile <- SPpairs$C_day1
str(Profile)
## List of 14
##  $ station         : int 112233
##  $ station_id      : chr "VIR112233"
##  $ datetime        : POSIXct[1:1], format: "2018-11-21 05:00:00"
##  $ latlon          : num [1, 1:2] 52.3 -119
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr ""
##   .. ..$ : chr [1:2] "latitude" "longitude"
##  $ elev            : int 1917
##  $ angle           : num 0
##  $ aspect          : num 0
##  $ hs              : num 111
##  $ type            : chr "vstation"
##  $ layers          :Classes 'snowprofileLayers' and 'data.frame':    30 obs. of  11 variables:
##   ..$ height     : num [1:30] 1.8 3.54 5.08 5.42 6.83 ...
##   ..$ ddate      : POSIXct[1:30], format: "2018-09-12 17:00:00" "2018-09-15 17:00:00" ...
##   ..$ density    : int [1:30] 422 354 408 332 404 357 313 294 312 359 ...
##   ..$ temperature: num [1:30] -0.1 -0.1 -0.2 -0.2 -0.3 -1 -1.1 -1.1 -1.4 -1.5 ...
##   ..$ lwc        : int [1:30] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$ gsize      : num [1:30] 1.37 1.62 0.96 1.69 0.82 0.98 1.38 1.93 1.25 1.32 ...
##   ..$ hardness   : num [1:30] 4 5 4 2 4 5 5 5 2.15 3 ...
##   ..$ ssi        : num [1:30] 5.43 3.93 1.37 1.39 4.12 5.59 4.68 1.57 3.4 3.03 ...
##   ..$ gtype      : Factor w/ 8 levels "DF","DH","FC",..: 5 6 4 2 4 6 6 6 3 4 ...
##   ..$ depth      : num [1:30] 109 107 106 105 104 ...
##   ..$ thickness  : num [1:30] 1.8 1.74 1.54 0.34 1.41 ...
##  $ zone            : chr "BONE_NORTH"
##  $ band            : chr "TL"
##  $ date            : Date[1:1], format: "2018-11-21"
##  $ maxObservedDepth: num 111
##  - attr(*, "class")= chr "snowprofile"

Check out an empty snowprofile object to understand which information can potentially go where:

snowprofile(dropNAs = FALSE)
##                  MetaData
## station                NA
## station_id             NA
## datetime               NA
## latlon              NA NA
## elev                   NA
## angle                  NA
## aspect                 NA
## hs                     NA
## maxObservedDepth       NA
## type               manual
## band                   NA
## zone                   NA
## comment                NA
## hn24                   NA
## hn72                   NA
## ski_pen                NA
## date                   NA
## layers
##   depth height thickness temperature density lwc gsize gsize_max gsize_avg
## 1    NA     NA        NA          NA      NA  NA    NA        NA        NA
##   gtype gtype_sec hardness ddate bdate datetag ssi sphericity v_strain_rate
## 1  <NA>      <NA>       NA  <NA>  <NA>    <NA>  NA         NA            NA
##   crit_cut_length tsa tsa_interface rta rta_interface layerOfInterest comment
## 1              NA  NA            NA  NA            NA              NA    <NA>
## tests
## [1] type       result     score      fract_char depth      comment   
## <0 rows> (or 0-length row.names)
## instabilitySigns
## [1] type    present comment
## <0 rows> (or 0-length row.names)

2. Profile creation

The package includes functions to create a snowprofile object by importing common file formats as well as provides constructor functions to manually create a snowprofile.

2.1 Import profiles from file

Import functions for generic snow profiles include::

and import functions for simulated profiles produced with the snow cover model SNOWPACK include:

Note that prf and pro files contain multiple profiles and thus the import functions return a snowprofileSet, while CAAML, csv, and sno files return a single snowprofile.

## Import a CAAML file
# Filename <- "path/to/file.caaml"
# Profile <- snowprofileCaaml(Filename)

# ## Import all profiles from a directory of CAAML files and create a `snowprofileSet`
# CaamlFiles <- list.files('path/to/caamlprofiles', full.names = T)
# Profiles <- lapply(CaamlFiles, snowprofileCaaml)
# Profiles <- snowprofileSet(Profiles)

An additional parser readSmet is also provided to read other input and output files from SNOWPACK.

2.2 Manually construct profiles

To manually create a snowprofile object see the help pages for the constructor functions snowprofile() and snowprofileLayers() where metadata and layer properties are provided as function arguments.

2.3 Formatting profiles

Import and constructor functions perform several validation checks for consistent structure (e.g. variable names, consistent layer thickness/depth/height). Sometimes profiles may be malformatted (e.g. files have different formatting than the functions in this package, future changes to this R package), so this package provides functions for checking profiles for formatting discrepancies and reformatting them if necessary. validate_snowprofile raises errors (or silently print error messages) in case of formatting discrepancies and reformat_snowprofile can conveniently correct data types or rename metadata / layer properties. See examples in the help files for these functions for examples of how it identify and correct errors in malformatted profiles

3. Profile manipulation

print, summary, and rbind methods exist to summarize and extract contents from snowprofile and snowprofileSet objects.

3.1 Print

Print contents of a snowprofile to the console.

Profile <- SPpairs$A_manual
print(Profile)
##                                    MetaData
## station                   SarpOfficeStation
## station_id                    niviz_station
## datetime                2019-02-22 09:24:00
## latlon              49.2765643 -122.9139272
## elev                                    339
## angle                                    38
## aspect                                  180
## hs                                      260
## type                                 manual
## observer                            Sarp|FH
## comment_general  don't feel like commenting
## comment_location            test caaml file
## date                             2019-02-22
## maxObservedDepth                        260
## layers
##    depth thickness height hardness gtype gsize_avg gsize_max lwc gtype_sec
## 1    250        10     10     5.00  MFcr       1.5       1.5   D      <NA>
## 2    230        20     30     3.00    FC       2.0       2.0   D      <NA>
## 3    220        10     40     5.00  MFcr       1.5       1.5   D      <NA>
## 4    157        63    103     4.00    RG       0.3       0.3   D      <NA>
## 5    155         5    105     2.00    SH       5.0       5.0   D      <NA>
## 6     95        60    165     4.00    RG       0.3       0.3   D      <NA>
## 7     75        20    185     3.00    RG       0.3       0.3   D      <NA>
## 8     60        15    200     1.00    FC       2.0       2.0   D      <NA>
## 9     42        18    218     2.00    RG       1.0       1.0   D      <NA>
## 10    40         2    220     1.50    FC       2.0       2.5   D      <NA>
## 11    38         2    222     1.00    SH       5.0       5.0   D        DH
## 12     0        35    260     1.25    DF       1.5       1.5   D      <NA>
##         ddate
## 1        <NA>
## 2        <NA>
## 3        <NA>
## 4        <NA>
## 5        <NA>
## 6        <NA>
## 7        <NA>
## 8        <NA>
## 9        <NA>
## 10       <NA>
## 11 2019-02-20
## 12       <NA>

3.2 Summary

Extract metadata from a single profile or set of profiles into a data.frame.

summary(Profile)
##             station    station_id            datetime      lat       lon elev
## 1 SarpOfficeStation niviz_station 2019-02-22 09:24:00 49.27656 -122.9139  339
##   angle aspect  hs   type nLayers observer            comment_general
## 1    38    180 260 manual      12  Sarp|FH don't feel like commenting
##   comment_location       date maxObservedDepth
## 1  test caaml file 2019-02-22              260
summary(SPgroup)
##    station station_id            datetime     lat      lon elev angle aspect
## 1   116093  VIR116093 2019-02-02 05:00:00 52.4650 -118.993 1907     0      0
## 2   116094  VIR116094 2019-02-02 05:00:00 52.4692 -118.959 1979     0      0
## 3   116095  VIR116095 2019-02-02 05:00:00 52.4733 -118.924 2011     0      0
## 4   116096  VIR116096 2019-02-02 05:00:00 52.4774 -118.889 1859     0      0
## 5   116643  VIR116643 2019-02-02 05:00:00 52.4821 -119.035 1867     0      0
## 6   116644  VIR116644 2019-02-02 05:00:00 52.4863 -119.000 1969     0      0
## 7   116645  VIR116645 2019-02-02 05:00:00 52.4904 -118.965 2019     0      0
## 8   116646  VIR116646 2019-02-02 05:00:00 52.4945 -118.931 1983     0      0
## 9   117194  VIR117194 2019-02-02 05:00:00 52.5033 -119.042 1915     0      0
## 10  117197  VIR117197 2019-02-02 05:00:00 52.5157 -118.937 1941     0      0
## 11  117746  VIR117746 2019-02-02 05:00:00 52.5287 -119.014 1911     0      0
## 12  117747  VIR117747 2019-02-02 05:00:00 52.5328 -118.979 1957     0      0
##        hs     type nLayers    zone band       date maxObservedDepth
## 1  156.48 vstation      50 ALBREDA   TL 2019-02-02           156.48
## 2  150.41 vstation      44 ALBREDA   TL 2019-02-02           150.41
## 3  117.89 vstation      35 ALBREDA   TL 2019-02-02           117.89
## 4  102.42 vstation      35 ALBREDA   TL 2019-02-02           102.42
## 5  179.87 vstation      45 ALBREDA   TL 2019-02-02           179.87
## 6  179.22 vstation      55 ALBREDA   TL 2019-02-02           179.22
## 7  156.85 vstation      39 ALBREDA   TL 2019-02-02           156.85
## 8  113.26 vstation      32 ALBREDA   TL 2019-02-02           113.26
## 9  156.02 vstation      44 ALBREDA   TL 2019-02-02           156.02
## 10 116.94 vstation      37 ALBREDA   TL 2019-02-02           116.94
## 11 142.78 vstation      41 ALBREDA   TL 2019-02-02           142.78
## 12 128.56 vstation      34 ALBREDA   TL 2019-02-02           128.56

Summary methods are useful to extract subsets of a snowprofileSet based on some attribute (e.g. location, time).

## Subset all profiles from SPgroup with elevation > 2000 m
Metadata <- summary(SPgroup)
Alpine <- SPgroup[Metadata$elev > 2000]
print(paste(length(Alpine), 'of', length(SPgroup), 'profiles in SPgroup are above 2000 m'))
## [1] "2 of 12 profiles in SPgroup are above 2000 m"

3.3 Rbind

rbind methods merge metadata and layer properties from one or many profiles into a large data.frame that is convenient for analysis tasks.

## Rbind SPgroup 
TabularProfile <- rbind(SPgroup)
names(TabularProfile)
##  [1] "station"          "station_id"       "datetime"         "lat"             
##  [5] "lon"              "elev"             "angle"            "aspect"          
##  [9] "hs"               "type"             "nLayers"          "zone"            
## [13] "band"             "date"             "maxObservedDepth" "height"          
## [17] "ddate"            "density"          "temperature"      "lwc"             
## [21] "gtype"            "hardness"         "ssi"              "gsize"           
## [25] "depth"            "thickness"
## Tabulate all grain types
table(TabularProfile$gtype)
## 
##   DF   DH   FC FCxr   MF MFcr   PP   RG   SH 
##   29   45  148   96    1   73   24   63   12
## Get elevations of profiles with SH layers > 5 mm
SH_layers <- subset(TabularProfile, gtype == 'SH' & gsize > 5)
sort(SH_layers$elev)
## [1] 1859 1911 1941 1957

4. Profile visualization

Plot methods exist for individual profiles and sets of profiles:

## Plot a single hardness profile
plot(Profile)

## Plot a timeline
plot(SPtimeline)

## Plot all profiles in the group and sort by height
plot(SPgroup, SortMethod = 'hs')

The package also includes several getColour... and setColour... functions to define colour palettes for various profile properties.