Throughout grwat
package documentation
a sample dataset spas
containing the daily runoff data for
Spas-Zagorye
gauge on Protva river
in Central European plane is used. The dataset is supplemented by
meteorological variables (temperature and precipitation) obtained from
CIRES-DOE (1880-1949) and ERA5 (1950-2021) data averaged inside gauge’s
basin:
library(grwat)
library(dplyr)
library(ggplot2)
library(lubridate)
data(spas)
head(spas)
#> # A tibble: 6 × 4
#> Date Q Temp Prec
#> <date> <dbl> <dbl> <dbl>
#> 1 1956-01-01 5.18 -6.46 0.453
#> 2 1956-01-02 5.18 -11.4 0.825
#> 3 1956-01-03 5.44 -10.7 0.26
#> 4 1956-01-04 5.44 -8.05 0.397
#> 5 1956-01-05 5.44 -11.7 0.102
#> 6 1956-01-06 5.58 -20.1 0.032
This 4-column representation is standard for advanced separation discussed below.
For more information on baseflow filtering, read the Baseflow filtering vignette.
grwat
implements several methods for
baseflow filtering. The get_baseflow()
function does the
job:
Qbase = gr_baseflow(spas$Q, method = 'lynehollick', a = 0.925, passes = 3)
head(Qbase)
#> [1] 3.698598 3.789843 3.876099 3.958334 4.037031 4.112454
Though get_baseflow()
needs just a vector of runoff
values, it can be applied in a traditional tidyverse pipeline like
follows:
# Calculate baseflow using Jakeman approach
hdata = spas %>%
mutate(Qbase = gr_baseflow(Q, method = 'jakeman'))
# Visualize for 2020 year
ggplot(hdata) +
geom_area(aes(Date, Q), fill = 'steelblue', color = 'black') +
geom_area(aes(Date, Qbase), fill = 'orangered', color = 'black') +
scale_x_date(limits = c(ymd(19800101), ymd(19801231)))
#> Warning: Removed 23376 rows containing non-finite values (`stat_align()`).
#> Removed 23376 rows containing non-finite values (`stat_align()`).
For more information on advanced separation, read the Advanced separation vignette.
Advanced separation by gr_separate()
implements the
method by (Rets et al. 2022), which
involves additional data on temperatures and precipitation to detect and
classify flood events into the rain, thaw and spring (seasonal thaw).
Between these events \(100\%\) of the
runoff is considered to be ground. Inside those events the ground flow
is filtered either by one of the baseflow functions, or by Kudelin’s
method, which degrades baseflow to \(0\) under the maximum runoff value during
the year.
The method is controlled by more than 20 parameters, which can be
region-specific. Therefore, to ease the management and distribution of
these parameters, they are organized as list, as returned by
gr_get_params()
:
sep = gr_separate(spas, params = gr_get_params(reg = 'center'))
#> grwat: data frame is correct
#> grwat: parameters list and types are OK
head(sep)
#> # A tibble: 6 × 11
#> Date Q Temp Prec Qbase Quick Qspri Qrain Qthaw Season Year
#> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 1956-01-01 5.18 -6.46 0.453 NA NA NA NA NA NA NA
#> 2 1956-01-02 5.18 -11.4 0.825 NA NA NA NA NA NA NA
#> 3 1956-01-03 5.44 -10.7 0.26 NA NA NA NA NA NA NA
#> 4 1956-01-04 5.44 -8.05 0.397 NA NA NA NA NA NA NA
#> 5 1956-01-05 5.44 -11.7 0.102 NA NA NA NA NA NA NA
#> 6 1956-01-06 5.58 -20.1 0.032 NA NA NA NA NA NA NA
Resulting separation can be visualized by gr_plot_sep()
function. In addition to classification of the flow, the function shows
the dates of the spring seasonal flood:
For more information on annual variables, read the Summaries vignette.
After hydrograph is separated, its characteristics can be summarized
by gr_summarize()
into annual variables which characterize
the annual runoff, its components (ground, spring, rain and thaw) and
low flow periods (summer and winter):
vars = gr_summarize(sep)
head(vars)
#> # A tibble: 6 × 57
#> Year Year1 Year2 Dspstart Dspend Tsp Qy Qspmax Dspmax Qygr
#> <dbl> <dbl> <dbl> <date> <date> <int> <dbl> <dbl> <date> <dbl>
#> 1 1956 1956 1957 1956-04-08 1956-05-05 27 18.4 467 1956-04-22 8.59
#> 2 1957 1957 1958 1957-03-25 1957-05-04 40 20.2 460 1957-04-08 9.77
#> 3 1958 1958 1959 1958-04-02 1958-05-13 41 27.3 537 1958-04-21 10.2
#> 4 1959 1959 1960 1959-03-28 1959-04-28 31 27.1 406 1959-04-16 10.9
#> 5 1960 1960 1961 1960-03-27 1960-04-27 31 29.6 406 1960-04-15 12.3
#> 6 1961 1961 1962 1961-03-07 1961-05-02 56 18.8 296 1961-04-10 10.8
#> # ℹ 47 more variables: Qsmin <dbl>, Dsmin <date>, Qwmin <dbl>, Dwmin <date>,
#> # Q30s <dbl>, D30s1 <date>, D30s2 <date>, Q30w <dbl>, D30w1 <date>,
#> # D30w2 <date>, Q10s <dbl>, D10s1 <date>, D10s2 <date>, Q10w <dbl>,
#> # D10w1 <date>, D10w2 <date>, Q5s <dbl>, D5s1 <date>, D5s2 <date>, Q5w <dbl>,
#> # D5w1 <date>, D5w2 <date>, Wy <dbl>, Wygr <dbl>, Wsp <dbl>, Wspgr <dbl>,
#> # Wsprngr <dbl>, Wrn <dbl>, Wrngr <dbl>, Wth <dbl>, Wthgr <dbl>, Wgrs <dbl>,
#> # Ws <dbl>, Wgrw <dbl>, Ww <dbl>, Qrnmax <dbl>, Qthmax <dbl>, …
These characteristics can be plotted by
gr_plot_vars()
:
gr_plot_vars(vars, D10w1, Wsprngr, Nthw, Qrnmax, tests = TRUE,
layout = matrix(1:4, nrow = 2, byrow = TRUE))
#> Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
#> Warning: Removed 1 rows containing missing values (`geom_point()`).
#> Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
#> Removed 1 rows containing non-finite values (`stat_smooth()`).
#> Warning: Removed 1 rows containing missing values (`geom_rect()`).
#> Warning: Removed 1 rows containing non-finite values (`stat_smooth()`).
grwat
contains some useful functions
that can facilitate your work with runoff data. In particular:
gr_report()
aggregates hydrograph separation and its
summaries into one information-rich graphical report that brings
everything into one place. Just pass the results of
gr_separate()
and gr_summarize()
into
gr_report()
function and provide the path to the output
HTML
file.
gr_get_gaps()
and gr_fill_gaps()
find
and interpolate the periods of missing runoff and meteorological data
which may affect the results.
gr_read_rean()
and gr_join_rean()
add
temperature and precipitation to your runoff data from daily reanalysis.
This can be useful if you do not have meteorological observations inside
the basin. Currently the East European plain is covered.
gr_plot_matrix()
, gr_plot_hori()
and
gr_plot_ridge()
empower daily runoff analysis with
fascinating graphical techniques which can be used to compare
hydrographs for different years: matrix plots, horizon plots and
ridgeline plots.
gr_set_locale()
translates plots and reports to the
specified language (English, Russian and Ukrainian are currently
available).
gr_plot_*
functions return silently ggplot2
objects or
the lists of such objects. This means that they can be modified to your
preferences before plotting. Just set print = FALSE
and
tweak aesthetics as you want or remove some information.