For a dataset as returned by get_xg3,
determine for each location the available multi-year XG3 series
and calculate summary statistics for each series.
Note that 'years' in this context always refers to hydroyears.
eval_xg3_series(data, xg3_type = c("L", "H", "V"), max_gap, min_dur)
| data | An object returned by |
|---|---|
| xg3_type | Character vector of length 1, 2 or 3.
Defines the types of XG3 which are taken from |
| max_gap | A positive integer (can be zero). It is part of what the user defines as 'an XG3 series': the maximum allowed time gap between two consecutive XG3 values in a series, expressed as the number of years without XG3 value. |
| min_dur | A strictly positive integer. It is part of what the user defines as 'an XG3 series': the minimum required duration of an XG3 series, i.e. the time (expressed as years) from the first to the last year of the XG3 series. |
A tibble with variables:
loc_code: see get_locs
xg3_variable: character; see Details
series: series ID, unique within loc_code
ser_length: series duration (as years), i.e. from first to
last year
ser_nryears: number of years in the series for which the
XG3 variable is available
ser_rel_nryears: the fraction ser_nryears / ser_length,
ser_firstyear: first year in the series with XG3 variable
ser_lastyear: last year in the series with XG3 variable
ser_pval_uniform: p-value of an exact, one-sample two-sided
Kolmogorov-Smirnov test for the discrete uniform distribution of the member
years within the XG3 series.
The smaller the p-value,
the less uniform the member years are spread within a series.
A perfectly uniform spread results in a p-value of 1.
Only with larger values of max_gap this p-value can get low.
Summary statistics based on the XG3 values, i.e. excluding the 'combined' series:
ser_mean: mean XG3 value of the series, as meters.
ser_sd: standard deviation of the XG3 values of the series
(an estimate of the superpopulation's standard deviation).
As meters.
ser_se_6y: estimated standard error of the mean XG3 for a
six-year period, applying finite population correction
(i.e. for design-based estimation of this mean).
Hence, ser_se_6y is zero when a series has no missing years.
As meters.
For series shorter than six years, the estimation is still regarding a
six-year period, assuming the same sampling variance as in the short
series.
ser_rel_sd_lcl: relative standard deviation of XG3 values,
i.e. ser_sd / ser_mean.
This value is only calculated for vert_crs = "local".
ser_rel_se_6y_lcl:
relative standard error of the mean XG3 for a
six-year period,
i.e. ser_se_6y / ser_mean.
This value is only calculated for vert_crs = "local".
An XG3 series is a location-specific, multi-year series of
LG3, HG3 and/or VG3 variables
and is user-restricted by max_gap (maximum allowed number of empty
years between 'series member' years) and
min_dur (minimum required length of the series).
Further, given a dataset of XG3 values per year and location, XG3 series are
always constructed as long as possible given the aforementioned
restrictions.
For one location and XG3 variable, more than one such XG3 series may be
available, which implies that those XG3 series are separated by more years
than the
value of max_gap.
The function returns summary statistics for the XG3 series that are available
in the dataset.
The XG3 series of each site
are numbered as 'prefix_series1', 'prefix_series2' with the
prefix being the value of xg3_variable.
The column xg3_variable in the resulting tibble
stands for the XG3 type + the vertical CRS (see get_xg3)
to which a series belongs.
xg3_variable is restricted to the requested XG3 types (LG3, HG3
and/or VG3) via the xg3_type argument, but adds an extra level
"combined" whenever the combination of data (which may have
both
vertical CRSes) and xg3_type implies more than one requested
variable.
This 'combined' level defines an XG3 series as an XG3 series where each
'member' year has all selected XG3 variables available.
Other functions to evaluate locations:
eval_chem(),
eval_xg3_avail()
if (FALSE) { watina <- connect_watina() library(dplyr) mylocs <- get_locs(watina, area_codes = "KAL") mydata <- mylocs %>% get_xg3(watina, 1900) mydata %>% arrange(loc_code, hydroyear) mydata %>% eval_xg3_series(xg3_type = c("L", "V"), max_gap = 2, min_dur = 5) # Disconnect: dbDisconnect(watina) }