Type:	Package
Title:	River Hydrograph Separation and Analysis
Version:	0.0.4
Date:	2023-10-27
Description:	River hydrograph separation and daily runoff time series analysis. Provides various filters to separate baseflow and quickflow. Implements advanced separation technique by Rets et al. (2022) <doi:10.1134/S0097807822010146> which involves meteorological data to reveal genetic components of the runoff: ground, rain, thaw and spring (seasonal thaw). High-performance C++17 computation, annually aggregated variables, statistical testing and numerous plotting functions for high-quality visualization.
License:	MIT + file LICENSE
URL:	https://github.com/tsamsonov/grwat
BugReports:	https://github.com/tsamsonov/grwat/issues
Encoding:	UTF-8
Language:	en-US
LinkingTo:	Rcpp
SystemRequirements:	pandoc
LazyData:	true
RoxygenNote:	7.2.3
VignetteBuilder:	knitr
Depends:	R (≥ 3.3)
Imports:	cli, Rcpp, magrittr, dplyr, tidyr, lubridate, stringr, rlang, grid, ggplot2, zoo, trend, mblm, R.utils
Suggests:	ggridges, ggHoriPlot, ggthemes, kableExtra, knitr, ncdf4, rmarkdown, sf, testthat, stringi
NeedsCompilation:	yes
Packaged:	2023-10-27 09:42:59 UTC; tsamsonov
Author:	Timofey Samsonov [aut, cre], Ekaterina Retz [ctb], Maria Kireeva [ctb]
Maintainer:	Timofey Samsonov <tsamsonov@geogr.msu.ru>
Repository:	CRAN
Date/Publication:	2023-10-27 11:40:12 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Extract baseflow

Description

Extract baseflow from hydrological series using the filtering approach

Usage

gr_baseflow(
  Q,
  a = 0.925,
  k = 0.975,
  C = 0.05,
  aq = -0.5,
  passes = 3,
  padding = 30,
  method = "lynehollick"
)

Arguments

Value

Numeric baseflow vector with length equal to Q

Examples

library(grwat)
library(ggplot2)
library(dplyr)
library(tidyr)
library(lubridate)

data(spas) # example Spas-Zagorye data is included with grwat package

# Calculate baseflow using Line-Hollick approach
hdata = spas %>% 
  mutate(Qbase = gr_baseflow(Q, method = 'lynehollick', 
                             a = 0.925, passes = 3))

# Visualize for 1980 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)))

# Compare various approaches
hdata = spas %>%
  mutate(lynehollick = gr_baseflow(Q, method = 'lynehollick', a = 0.9),
         boughton = gr_baseflow(Q, method = 'boughton', k = 0.9),
         jakeman = gr_baseflow(Q, method = 'jakeman', k = 0.9),
         maxwell = gr_baseflow(Q, method = 'maxwell', k = 0.9)) %>% 
  pivot_longer(lynehollick:maxwell, names_to = 'Method', values_to = 'Qbase')

# Visualize for 1980 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(19810101), ymd(19811231))) +
  facet_wrap(~Method)

# Compare Lyne to Kudelin
p = gr_get_params('center')
p$filter = 'kudelin'

hdata = spas %>% 
  mutate(lynehollick = gr_baseflow(Q, method = 'lynehollick', 
                                   a = 0.925, passes = 3),
         kudelin = gr_separate(spas, p)$Qbase) %>% 
  pivot_longer(lynehollick:kudelin, names_to = 'Method', values_to = 'Qbase')

# Visualize for 1980 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))) +
  facet_wrap(~Method)

Quasi-geographic buffering

Description

Generate the buffer of spatial object in geographic coordinates. The function transforms the object into Azimuthal equidistant projection, then buffers it by the specified radius and then reprojects into geographical coordinate system (WGS84)

Usage

gr_buffer_geo(g, bufsize)

Arguments

Value

sf or sfg object, buffered to bufsize and projected into geographic coordinates (WGS84).

Examples

if (require("sf")) {

  library(grwat)
  library(ggplot2)
  path = system.file("extdata", "spas-zagorye.gpkg", package = "grwat")
  basin = sf::st_read(path, layer = 'basin') # read basin region

  basin_buffer = gr_buffer_geo(basin, 25000)

  ggplot() +
    geom_sf(data = basin_buffer, fill = 'orangered', color = 'black') +
    geom_sf(data = basin, fill = 'steelblue', color = 'black')

}

Check the correctness of data frame for separating

Description

This function is called inside gr_separate(), but can be used explicitly inside your code.

Usage

gr_check_data(df)

Arguments

Value

stops execution if df contains the wrong number of columns, or the columns have the wrong types, or the data in columns is incorrect (e.g. runoff or precipitation are negative).

Examples

library(grwat)

# example Spas-Zagorye data is included with grwat package
data(spas)
head(spas)

gr_check_data(spas)

# raw Spas-Zagorye data represents date components
# in columns and does not contain meteorologgical variables
path = system.file("extdata", "spas-zagorye.txt", 
                   package = "grwat")

hdata_raw = read.delim(path, header = FALSE, 
                       sep = ' ', na.strings = c('-999', '-999.0', '-'),
                       col.names = c('d', 'm', 'y', 'q'))

print(hdata_raw)

try(gr_check_data(hdata_raw))

Check the correctness of parameters list for separating

Description

Check the correctness of parameters list for separating

Usage

gr_check_params(params, df = NULL)

Arguments

Value

stops the execution if anything is wrong and prints the exact reason of the error. Otherwise prints the message that everything is OK

Examples

library(grwat)

# example Spas-Zagorye data is included with grwat package
data(spas)

params = gr_get_params(reg = 'center')

gr_check_params(params)

# set the unknown parameter
params$new = -2

# use try if you do not want to stop at error
try(gr_check_params(params))

# remove wrong parameter
params$new = NULL

# remove right parameter
params$grad1 = NULL
try(gr_check_params(params))

# reset
params = gr_get_params(reg = 'center')

sep = gr_separate(spas, params, debug = TRUE)
parlist = attributes(sep)$params

parlist[['2002']]$grad1 = 4

# if the parlist is used for separation
# then data frame must be specified
try(gr_check_params(parlist))

gr_check_params(parlist, spas)

# grad parameter is not known
parlist[['2002']]$grad = 4
try(gr_check_params(parlist, spas))

# remove wrong parameter
parlist[['2002']]$grad = NULL

# remove year
parlist[['2002']] = NULL
try(gr_check_params(parlist, spas))

parlist[['2002']] = parlist[['2001']]
gr_check_params(parlist, spas)

Fill missing daily data

Description

Use the function to fill the missing daily data by linear interpolation. These can be both missing dates and missing runoff or temperature values. A preliminary summary of missing data can be viewed by gr_get_gaps()

Usage

gr_fill_gaps(hdata, autocorr = 0.7, nobserv = NULL)

Arguments

Value

data.frame which is a filled version of hdata

Examples

library(grwat)
library(dplyr)

# example Spas-Zagorye data is included with grwat package
path = system.file("extdata", "spas-zagorye.txt", 
                   package = "grwat")

hdata_raw = read.delim(path, header = FALSE, 
                       sep = ' ', na.strings = c('-999', '-999.0', '-'),
                       col.names = c('d', 'm', 'y', 'q'))

hdata = hdata_raw %>% 
  transmute(Date = lubridate::make_date(y, m, d), 
            Q = q)

head(hdata)

# identify gaps
gr_get_gaps(hdata) 

# fill gaps
fhdata = gr_fill_gaps(hdata, autocorr = 0.8)

# check the results
gr_get_gaps(fhdata)

# fill gaps
fhdata = gr_fill_gaps(hdata, nobserv = 7)

# check the results
gr_get_gaps(fhdata)

Get gaps in the daily data

Description

Use the function to detect periods of missing data. The first column must be of Date type. The data is considered to be a gap if any value in the row is missing.

Usage

gr_get_gaps(hdata)

Arguments

Value

data.frame with periods of data and periods of gaps, containing five columns: number of the period (num), start of the period (start_date), end of the period (end_date), duration of the period (duration) and type of the period (type).

Examples

library(grwat)
library(dplyr)

# example Spas-Zagorye data is included with grwat package
path = system.file("extdata", "spas-zagorye.txt", 
                   package = "grwat")

hdata_raw = read.delim(path, header = FALSE, 
                       sep = ' ', na.strings = c('-999', '-999.0', '-'),
                       col.names = c('d', 'm', 'y', 'q'))

hdata = hdata_raw %>% 
  transmute(Date = lubridate::make_date(y, m, d), 
            Q = q)

head(hdata)

# identify gaps
gr_get_gaps(hdata) 

# fill gaps
fhdata = gr_fill_gaps(hdata, autocorr = 0.8)

# check the results
gr_get_gaps(fhdata)

# fill gaps
fhdata = gr_fill_gaps(hdata, nobserv = 7)

# check the results
gr_get_gaps(fhdata)

Get hydrograph separation parameters

Description

The function returns the list of parameters that can be used by gr_separate(). Since the parameters are region-specific, the location must be selected. It can be identified by region name or geographic coordinates. If both are specified, then region have a higher priority

Usage

gr_get_params(reg = "center", lon = NULL, lat = NULL)

Arguments

Value

List of separation parameters that can be used in gr_separate() function.

Examples

library(grwat)

params = gr_get_params(reg = 'center')

print(params)

Get the information about parameters used to separate the hydrograph

Description

Get the information about parameters used to separate the hydrograph

Usage

gr_help_params()

Value

data.frame with description of hydrograph separation parameters that are used in gr_separate() .

Examples

library(grwat)

gr_help_params()

Hydrograph separation variables

Description

Use this function to learn the meaning of the variables that are calculated by gr_summarize().

Usage

gr_help_vars()

Value

data.frame of hydrograph separation variables

Examples

library(grwat)

gr_help_vars()

Join reanalysis data

Description

The function performs spatial join of meteorological variables (temperature and precipitation) from grwat reanalysis to the daily runoff time series. Reanalysis covers the East European Plain with 0.75 degrees spatial resolution and is obtained based on CIRES-DOE (1880-1949) and ERA5 (1950-2021) data. This function is useful when the data from meteorological stations are missing inside the basin.

Usage

gr_join_rean(hdata, rean, buffer)

Arguments

Details

Download the reanalysis archive from here.

Value

data.frame with four columns: date, runoff, temperature, precipitation.

Examples

if (require("sf") && require("ncdf4")) {

  library(grwat)
  library(dplyr)

  # example Spas-Zagorye daily runoff data is included with grwat package
  data_path = system.file("extdata", "spas-zagorye.txt",
                          package = "grwat")

  hdata_raw = read.delim(data_path, header = FALSE, 
                         sep = ' ', na.strings = c('-999', '-999.0', '-'),
                         col.names = c('d', 'm', 'y', 'q'))

  hdata = hdata_raw %>%
    transmute(Date = lubridate::make_date(y, m, d),
              Q = q)

  head(hdata)

  # read basin
  basin_path = system.file("extdata", "spas-zagorye.gpkg",
                           package = "grwat")
  basin = sf::st_read(basin_path, layer = 'basin') # read basin region
  basin_buffer = gr_buffer_geo(basin, 25000)

  ## Not run: 
    # read reanalysis data
    rean = gr_read_rean(
      '/Volumes/Data/Spatial/Reanalysis/grwat/pre_1880-2021.nc',
      '/Volumes/Data/Spatial/Reanalysis/grwat/temp_1880-2021.nc'
    )

    # spatial join of reanalysis data to runoff data
    hdata_rean = gr_join_rean(hdata, rean, basin_buffer)

    head(hdata_rean)
  
## End(Not run)

}

Tabular representation of tests

Description

This function is used to represent the results of gr_test_vars() in a tabular form. Used mainly in gr_report(), but can be used for your own purposes.

Usage

gr_kable_tests(tests, format = "html")

Arguments

Value

HTML table as returned by knitr::kable() function.

Examples

if (require("kableExtra")) {
  
  library(grwat)
  
  data(spas) # example Spas-Zagorye data is included with grwat package
  
  # separate
  sep = gr_separate(spas, params = gr_get_params(reg = 'center'))
  
  # summarize from 1965 to 1990
  vars = gr_summarize(sep, 1965, 1990)
  
  # test all variables
  tests = gr_test_vars(vars)
  
  # kable tests
  gr_kable_tests(tests)

}

Plot runoff ACF

Description

The function plots the autocorrelation function (ACF) for daily runoff time series. A number of days corresponding to the specified autocorr value is highlighted.

Usage

gr_plot_acf(hdata, autocorr = 0.7, maxlag = 30, print = TRUE)

Arguments

Value

ggplot2 object representing the autocorrelation function (ACF) for daily runoff time series

Examples

library(grwat)

# example Spas-Zagorye data is included with grwat package
data(spas)
head(spas)

# plot ACF
gr_plot_acf(spas, 0.65)

Horizon hydrograph plot

Description

A convenient wrapper around ggHoriPlot::geom_horizon() to visualize multiple river hydrographs at once.

Usage

gr_plot_hori(df, years, pal = "Blues", rev = TRUE, scale = 6, print = TRUE)

Arguments

Value

ggplot2 object representing multiple river hydrographs at once using the horizon plot approach

Examples

if (require("ggHoriPlot") && require("ggthemes")) {
  
  library(grwat)
  
  data(spas) # example Spas-Zagorye data is included with grwat package
  
  # separate
  sep = gr_separate(spas, params = gr_get_params(reg = 'center'))
  
  # horizon plot for selected years
  gr_plot_hori(sep, years = 1960:1980)
  
}

Runoff matrix plot

Description

The function plots runoff values, components and seasons using the matrix-based approach. The X axis corresponds to the day of the year, and the Y axis corresponds to the year. The function is useful when the whole picture of river runoff needs to be assessed.

Usage

gr_plot_matrix(df, years = NULL, type = "runoff", print = TRUE)

Arguments

Value

ggplot2 object representing the runoff values, components or seasons using the matrix-based approach

Examples

library(grwat)

data(spas) # example Spas-Zagorye data is included with grwat package

# separate
sep = gr_separate(spas, params = gr_get_params(reg = 'center'))

# matrix plot for runoff
gr_plot_matrix(sep, type = 'runoff')

# matrix plot for seasons
gr_plot_matrix(sep, type = 'season')

# matrix plot for genetic components
gr_plot_matrix(sep, type = 'component')

Plot minimum runoff month

Description

Generate a histogram of a minimum runoff month for two periods: before and after the change year set by year parameter.

Usage

gr_plot_minmonth(
  df,
  year = NULL,
  exclude = NULL,
  tests = NULL,
  pagebreak = FALSE,
  print = TRUE
)

Arguments

Value

list of two ggplot2 objects, representing the histogram of a minimum runoff month for two periods: before and after the change year

Examples

library(grwat)

data(spas) # example Spas-Zagorye data is included with grwat package

# separate
sep = gr_separate(spas, params = gr_get_params(reg = 'center'))

# summarize from 1965 to 1990
vars = gr_summarize(sep, 1965, 1990)

# plot minimum runoff month for two periods divided by Pettitt test
gr_plot_minmonth(vars, tests = gr_test_vars(vars))

# plot minimum runoff month for two periods divided by fixed year
gr_plot_minmonth(vars, year = 1978)

Plot long-term hydrograph variable changes

Description

This function generates boxplots of the hydrograph separation variables produced by gr_summarize(). The data for each variable is divided into two samples: before and after the change year either set by year parameter or extracted from tests (statistically estimated). Different background fill colors are used to differentiate seasons types.

Usage

gr_plot_periods(
  df,
  ...,
  year = NULL,
  exclude = NULL,
  tests = NULL,
  layout = as.matrix(1),
  pagebreak = FALSE,
  print = TRUE
)

Arguments

Value

list of ggplot2 objects, one for each variable, representing its long-term changes

Examples

library(grwat)

data(spas) # example Spas-Zagorye data is included with grwat package

# separate
sep = gr_separate(spas, params = gr_get_params(reg = 'center'))

# summarize from 1965 to 1990
vars = gr_summarize(sep, 1965, 1990)

# plot periods with fixed change year
gr_plot_periods(vars, Qygr, year = 1978)

# plot periods with change year from Pettitt test
gr_plot_periods(vars, Qygr, tests = TRUE)

# calculate test beforehand
tests = gr_test_vars(vars)
gr_plot_periods(vars, Qspmax, tests = tests)

# use matrix layout to plot multiple variables
gr_plot_periods(vars, Qygr, Qspmax, D10w1, Wsprngr,
                layout = matrix(1:4, nrow = 2),
                tests = tests)

Ridgeline hydrograph plot

Description

A convenient wrapper around ggridges::geom_ridgeline() to visualize multiple river hydrographs at once.

Usage

gr_plot_ridge(
  df,
  years,
  pal = 4,
  rev = FALSE,
  scale = 0.01,
  alpha = 0.8,
  print = TRUE
)

Arguments

Value

ggplot2 object representing the multiple river hydrographs at once using the ridgeline plot approach

Examples

if (require("ggridges")) {
  
  library(grwat)
  
  data(spas) # example Spas-Zagorye data is included with grwat package
  
  # separate
  sep = gr_separate(spas, params = gr_get_params(reg = 'center'))
  
  # ridgline plot for selected years
  gr_plot_ridge(sep, years = c(1960, 1965, 1989, 2001, 2012)) 

}

Plot hydrograph separation

Description

The function plots river hydrograph by filling the different flow types using colors. Matrix layouts can be used if multiple plots are needed. Temperature and precipitation can be overlaid.

Usage

gr_plot_sep(
  df,
  years = NULL,
  layout = as.matrix(1),
  pagebreak = FALSE,
  temp = FALSE,
  prec = FALSE,
  span = 5,
  print = TRUE
)

Arguments

Value

list of ggplot2 objects, one for each year, representing the hydrograph separation

Examples

library(grwat)

data(spas) # example Spas-Zagorye data is included with grwat package

# separate
sep = gr_separate(spas, params = gr_get_params(reg = 'center'))

# One year
gr_plot_sep(sep, 1978) 

# Two years
gr_plot_sep(sep, c(1978, 1989)) 

# Two years in a matrix layout
gr_plot_sep(sep, 1988:1989, layout = matrix(1:2, nrow = 2, byrow = TRUE)) 

# Add temperature and precipitation
gr_plot_sep(sep, 1991, temp = TRUE, prec = TRUE) 

Plot change year density

Description

The function extracts change years from results of gr_test_vars() and plots their probability density. Since for every variable the change year is individual, this procedure allows finding the one most probable year, which is the mode of the distribution. This year is highlighted by the line and labeled on the plot.

Usage

gr_plot_tests(tests, type = "year", print = TRUE)

Arguments

Value

ggplot2 object representing the selected type of the tested variable

Examples

library(grwat)

data(spas) # example Spas-Zagorye data is included with grwat package

# separate
sep = gr_separate(spas, params = gr_get_params(reg = 'center'))

# summarize from 1965 to 1990
vars = gr_summarize(sep, 1965, 1990)

# test all variables
tests = gr_test_vars(vars)

# plot change year from Pettitt test
gr_plot_tests(tests, type = 'year')

Plot interannual hydrograph variable changes

Description

This function plots the hydrograph separation variables produced by gr_summarize(). Different background fill colors and line types are used to differentiate seasons and variable types.

Usage

gr_plot_vars(
  df,
  ...,
  tests = NULL,
  exclude = NULL,
  smooth = TRUE,
  layout = as.matrix(1),
  pagebreak = FALSE,
  print = TRUE
)

Arguments

Value

list of ggplot2 objects, one for each variable, representing its interannual changes

Examples

library(grwat)

data(spas) # example Spas-Zagorye data is included with grwat package

# separate
sep = gr_separate(spas, params = gr_get_params(reg = 'center'))

# summarize from 1965 to 1990
vars = gr_summarize(sep, 1965, 1990)

# plot one selected variable
gr_plot_vars(vars, Qygr) 

# plot two variables sequentially
gr_plot_vars(vars, D10w1, Wsprngr)

# four variables in matrix layout with tests calculated on the fly
gr_plot_vars(vars, Qspmax, Qygr, D10w1, Wsprngr,
             layout = matrix(1:4, nrow = 2, byrow = TRUE),
             tests = TRUE) 

Read reanalysis data

Description

The function reads meteorological variables (temperature and precipitation) from grwat reanalysis for using with gr_join_rean(). Reanalysis covers the East European Plain with 0.75 degrees spatial resolution and is obtained based on CIRES-DOE (1880-1949) and ERA5 (1950-2021) data.

Usage

gr_read_rean(file_prec, file_temp)

Arguments

Details

Download the reanalysis archive from here.

Value

list containing time series, precipitation series, temperature series and spatial points (sf)

Examples

if (require("sf") && require("ncdf4")) {
  
  library(grwat)
  
  # read reanalysis data
  ## Not run: 
    rean = gr_read_rean(
      '/Volumes/Data/Spatial/Reanalysis/grwat/pre_1880-2021.nc',
      '/Volumes/Data/Spatial/Reanalysis/grwat/temp_1880-2021.nc'
    ) 
    
    str(rean)
  
## End(Not run)
  
}

Report hydrograph separation and variables

Description

This function generates a graphical HTML report that summarizes separation of hydrograph, its variables and their statistical properties. See example report generated by this command for spas dataset included in grwat package.

Usage

gr_report(
  sep,
  vars,
  output = "Report.html",
  year = NULL,
  exclude = NULL,
  temp = FALSE,
  prec = FALSE,
  span = 5,
  locale = "EN"
)

Arguments

Value

No return value, called for side effects

Examples

## Not run: 
  if (require("knitr") && require("rmarkdown") && require("kableExtra")) {
    library(grwat)
    
    data(spas) # example Spas-Zagorye data is included with grwat package
    
    # separate
    sep = gr_separate(spas, params = gr_get_params(reg = 'center'))
    
    # summarize
    vars = gr_summarize(sep)
    
    # report
    report = '~/Spas-Zagorye.html'
    
    gr_report(sep, vars, output = report)
    browseURL(report)
  }

## End(Not run)

Advanced hydrograph separation

Description

Separates the runoff into genetic components: groundwater, thaw, rain and spring.

Usage

gr_separate(df, params = gr_get_params(), debug = FALSE)

Arguments

Value

A data.frame with 11 columns:

Examples

library(grwat)

data(spas) # example Spas-Zagorye data is included with grwat package
head(spas)

# separate
sep = gr_separate(spas, params = gr_get_params(reg = 'center'))

# Visualize
gr_plot_sep(sep, c(1978, 1989)) 

# Debug mode gives access to additional information
sep_debug = gr_separate(spas, 
                        params = gr_get_params(reg = 'center'), 
                        debug = TRUE)

# a vector of years with jittered params
jit = attributes(sep_debug)$jittered
print(jit)

# actual params used for each year
parlist = attributes(sep_debug)$params
partab = do.call(dplyr::bind_rows, parlist) # View as table
head(partab)

# extract and tweak parameters for selected year
p = parlist[['1989']]
p$grad1 = 1
p$grad2 = 2.5

# use tweaked parameters for all years
sep_debug = gr_separate(spas, params = p, debug = TRUE)

# Visualize
gr_plot_sep(sep_debug, c(1978, 1989)) 

# actual params used for each year
parlist = attributes(sep_debug)$params

# tweak parameters for selected year
parlist[['1989']]$grad1 = 3
parlist[['1989']]$grad2 = 6

# set the sprecdays parameter for multiple years
parlist = gr_set_param(parlist, sprecdays, 
                       years = c(1978, 1989:1995), 
                       value = 15)

# set the spcomp parameter for all years
parlist = gr_set_param(parlist, spcomp, value = 2.5)

# use the list of parameters for separation
sep_debug = gr_separate(spas, params = parlist, debug = TRUE)

# Visualize
gr_plot_sep(sep_debug, c(1978, 1989))

Set the language that is used for plotting

Description

Run this function once at the beginning of the session. All plots will be labeled using the selected language.

Usage

gr_set_locale(locale = "EN")

Arguments

Details

Note to Linux users: the desired locale may not be installed on the system. A list of available locales can be obtained in bash terminal:

locale -a

Russian locale is ru_RU.UTF-8, and Ukrainian locale is uk_UA.UTF-8. If absent in the list, then install the desired locales by:

⁠sudo locale-gen ru_RU.UTF-8⁠

⁠sudo locale-gen uk_UA.UTF-8⁠

⁠sudo update-locale⁠

Then restart R session, and localization should work as expected.

Value

No return value, called for side effects

Examples

library(grwat)

data(spas) # example Spas-Zagorye data is included with grwat package

# separate
sep = gr_separate(spas, params = gr_get_params(reg = 'center'))

# Default is  English
gr_set_locale('EN')
gr_plot_sep(sep, 1978) 

Set the value of hydrograph separation parameter

Description

The value is set for selected years in parameter list. Such list is returned by gr_separate() with debug = TRUE set.

Usage

gr_set_param(params, p, value, years = NULL)

Arguments

Value

list of lists — a modified version of params

Examples

library(grwat)

data(spas) # example Spas-Zagorye data is included with grwat package

# Debug mode gives access to additional information
sep = gr_separate(spas, 
                  params = gr_get_params(reg = 'center'), 
                  debug = TRUE)

# Visualize
gr_plot_sep(sep, c(1978, 1989)) 

# actual params used for each year
parlist = attributes(sep)$params

# set the sprecdays parameter for multiple years
parlist = gr_set_param(parlist, sprecdays, 
                       years = c(1978, 1989:1995), 
                       value = 15)

# use the list of parameters for separation
sep_new = gr_separate(spas, params = parlist, debug = TRUE)

# Visualize
gr_plot_sep(sep_new, c(1978, 1989))

Summarize hydrograph separation

Description

Use this function to get meaningful summary statistics for hydrograph separation. Resulting variables are described by gr_help_vars(). This function is a convenient wrapper around dplyr's df %>% group_by %>% summarize idiom.

Usage

gr_summarize(df, year_min = NULL, year_max = NULL)

Arguments

Value

data.frame with one row for each water-resources year and multiple columns of statistics explained by gr_help_vars().

Examples

library(grwat)

data(spas) # example Spas-Zagorye data is included with grwat package

# separate
sep = gr_separate(spas, params = gr_get_params(reg = 'center'))

# summarize
vars = gr_summarize(sep)

head(vars)

gr_plot_vars(vars, Qygr, tests = TRUE)

Test hydrograph changes

Description

Use this function to test interannual changes or hydrograph separation variables returned by gr_summarize(). Pettitt test is used to detect the change year — i.e. the year which divides the time series into the statistically most differing samples. Student (Welch) and Fisher tests are used to estimate the significance of mean and variance differences of these samples. Theil-Sen test calculates the trend slope value. Mann-Kendall test is performed to reveal the significance of the trend.

Usage

gr_test_vars(df, ..., year = NULL, exclude = NULL)

Arguments

Details

Number of observations formally required for various tests: Pettitt > 0, Mann-Kendall > 2, Theil-Sen > 1, Student > 1, Fisher > 1.

Value

list of testing results with following elements:

Examples

library(grwat)

data(spas) # example Spas-Zagorye data is included with grwat package

# separate
sep = gr_separate(spas, params = gr_get_params(reg = 'center'))

# summarize from 1965 to 1990
vars = gr_summarize(sep, 1965, 1990)

# test all variables
tests = gr_test_vars(vars)

# view Pettitt test for Qygr
tests$ptt$Qygr

# view Fisher test for Q30s
tests$ft$Q30s

# test only Qygr and Q30s using 1978 as fixed year and excluding 1988-1991 yrs
gr_test_vars(vars, Qygr, Q30s, year = 1978, exclude = 1981:1983)

Spas-Zagorye daily runoff data

Description

A dataset containing the daily runoff data for Spas-Zagorye gauge on Protva river in Central European plane. The dataset is supplemented by meteorological variables (temperature and precipitation) obtained from CIRES-DOE (1880-1949) and ERA5 (1950-2021) data.

Usage

spas

Format

A data frame with 23742 rows and 4 variables:

Date

date, in dates

Q

daily runoff, in m3/s

Temp

daily temperature, in Celsius degrees

Prec

daily precipitation, in mm

Source

https://allrivers.info/gauge/protva-obninsk

https://gmvo.skniivh.ru

https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5

https://psl.noaa.gov/data/gridded/data.20thC_ReanV3.html