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Type:	Package
Title:	River Hydrograph Separation and Analysis
Version:	0.1
Date:	2025-10-02
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,https://tsamsonov.github.io/grwat/
BugReports:	https://github.com/tsamsonov/grwat/issues
Encoding:	UTF-8
Language:	en-US
LinkingTo:	Rcpp
SystemRequirements:	pandoc
LazyData:	true
RoxygenNote:	7.3.3
VignetteBuilder:	knitr
Depends:	R (≥ 4.1)
Imports:	cli, Rcpp, 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:	2025-10-02 16:20:18 UTC; tsamsonov
Author:	Timofey Samsonov [aut, cre], Ekaterina Rets [ctb], Maria Kireeva [ctb]
Maintainer:	Timofey Samsonov <tsamsonov@geogr.msu.ru>
Repository:	CRAN
Date/Publication:	2025-10-02 17:50:09 UTC

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 toQ

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 approachhdata = spas |>   mutate(Qbase = gr_baseflow(Q, method = 'lynehollick',                              a = 0.925, passes = 3))# Visualize for 1980 yearggplot(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 approacheshdata = 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 yearggplot(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 Kudelinp = 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 yearggplot(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 orsfg object, buffered tobufsize 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 insidegr_separate(), but can be used explicitly inside your code.

Usage

gr_check_data(df)

Arguments

Value

stops execution ifdf 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 packagedata(spas)head(spas)gr_check_data(spas)# raw Spas-Zagorye data represents date components# in columns and does not contain meteorologgical variablespath = 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 packagedata(spas)params = gr_get_params(reg = 'center')gr_check_params(params)# set the unknown parameterparams$new = -2# use try if you do not want to stop at errortry(gr_check_params(params))# remove wrong parameterparams$new = NULL# remove right parameterparams$grad1 = NULLtry(gr_check_params(params))# resetparams = gr_get_params(reg = 'center')sep = gr_separate(spas, params, debug = TRUE)parlist = attributes(sep)$paramsparlist[['2002']]$grad1 = 4# if the parlist is used for separation# then data frame must be specifiedtry(gr_check_params(parlist))gr_check_params(parlist, spas)# grad parameter is not knownparlist[['2002']]$grad = 4try(gr_check_params(parlist, spas))# remove wrong parameterparlist[['2002']]$grad = NULL# remove yearparlist[['2002']] = NULLtry(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 bygr_get_gaps()

Usage

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

Arguments

Value

data.frame which is a filled version ofhdata

Examples

library(grwat)library(dplyr)# example Spas-Zagorye data is included with grwat packagepath = 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 gapsgr_get_gaps(hdata) # fill gapsfhdata = gr_fill_gaps(hdata, autocorr = 0.8)# check the resultsgr_get_gaps(fhdata)# fill gapsfhdata = gr_fill_gaps(hdata, nobserv = 7)# check the resultsgr_get_gaps(fhdata)

Convert data frame of parameters to list

Description

Convert data frame of parameters to list

Usage

gr_from_pardf(pardf)

Arguments

Value

list oflists of hydrograph separation parameters as returned inparams attribute bygr_separate() withdebug = TRUE.

Examples

library(grwat)data(spas) # example Spas-Zagorye data is included with grwat packagehead(spas)# separatesep = gr_separate(spas, params = gr_get_params(reg = 'center'))# Visualizegr_plot_sep(sep, c(1978, 1989)) # Debug mode gives access to additional informationsep_debug = gr_separate(spas,                         params = gr_get_params(reg = 'center'),                         debug = TRUE)# a vector of years with jittered paramsjit = attributes(sep_debug)$jitteredprint(jit)# actual params used for each yearparlist = attributes(sep_debug)$params# tweak parameters for selected yearparlist[['1989']]$grad1 = 3parlist[['1989']]$grad2 = 6# tabular representation of parameterspartab = gr_to_pardf(parlist)head(partab)partab |>   dplyr::filter(year == 1989) |>   head()parlist_back = gr_from_pardf(partab)head(parlist_back[['1989']])

Get gaps in the daily data

Description

Use the function to detect periods of missing data. The first column must be ofDate 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 packagepath = 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 gapsgr_get_gaps(hdata) # fill gapsfhdata = gr_fill_gaps(hdata, autocorr = 0.8)# check the resultsgr_get_gaps(fhdata)# fill gapsfhdata = gr_fill_gaps(hdata, nobserv = 7)# check the resultsgr_get_gaps(fhdata)

Get hydrograph separation parameters

Description

The function returns the list of parameters that can be used bygr_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 ingr_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 ingr_separate() .

Examples

library(grwat)gr_help_params()

Hydrograph separation variables

Description

Use this function to learn the meaning of the variables that are calculated bygr_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) fromgrwat 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 fromhere.

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 ofgr_test_vars() in a tabular form. Used mainly ingr_report(), but can be used for your own purposes.

Usage

gr_kable_tests(tests, format = "html")

Arguments

Value

HTML table as returned byknitr::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 specifiedautocorr 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 packagedata(spas)head(spas)# plot ACFgr_plot_acf(spas, 0.65)

Horizon hydrograph plot

Description

A convenient wrapper aroundggHoriPlot::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# separatesep = gr_separate(spas, params = gr_get_params(reg = 'center'))# matrix plot for runoffgr_plot_matrix(sep, type = 'runoff')# matrix plot for seasonsgr_plot_matrix(sep, type = 'season')# matrix plot for genetic componentsgr_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 byyear parameter.

Usage

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

Arguments

Value

list of twoggplot2 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# separatesep = gr_separate(spas, params = gr_get_params(reg = 'center'))# summarize from 1965 to 1990vars = gr_summarize(sep, 1965, 1990)# plot minimum runoff month for two periods divided by Pettitt testgr_plot_minmonth(vars, tests = gr_test_vars(vars))# plot minimum runoff month for two periods divided by fixed yeargr_plot_minmonth(vars, year = 1978)

Plot long-term hydrograph variable changes

Description

This function generates boxplots of the hydrograph separation variables produced bygr_summarize(). The data for each variable is divided into two samples: before and after the change year either set byyear parameter or extracted fromtests (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 ofggplot2 objects, one for each variable, representing its long-term changes

Examples

library(grwat)data(spas) # example Spas-Zagorye data is included with grwat package# separatesep = gr_separate(spas, params = gr_get_params(reg = 'center'))# summarize from 1965 to 1990vars = gr_summarize(sep, 1965, 1990)# plot periods with fixed change yeargr_plot_periods(vars, Qygr, year = 1978)# plot periods with change year from Pettitt testgr_plot_periods(vars, Qygr, tests = TRUE)# calculate test beforehandtests = gr_test_vars(vars)gr_plot_periods(vars, Qspmax, tests = tests)# use matrix layout to plot multiple variablesgr_plot_periods(vars, Qygr, Qspmax, D10w1, Wsprngr,                layout = matrix(1:4, nrow = 2),                tests = tests)

Ridgeline hydrograph plot

Description

A convenient wrapper aroundggridges::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,  yrange = "uniform")

Arguments

Value

list ofggplot2 objects, one for each year, representing the hydrograph separation

Examples

library(grwat)data(spas) # example Spas-Zagorye data is included with grwat package# separatesep = gr_separate(spas, params = gr_get_params(reg = 'center'))# One yeargr_plot_sep(sep, 1978) # Two yearsgr_plot_sep(sep, c(1978, 1989)) # Two years in a matrix layoutgr_plot_sep(sep, 1987:1988, layout = matrix(1:2, nrow = 2, byrow = TRUE)) # Four years in a matrix layout with free Y scalegr_plot_sep(sep, 1987:1990, layout = matrix(1:4, nrow = 2, byrow = TRUE), yrange = 'each') # Add temperature and precipitationgr_plot_sep(sep, 1991, temp = TRUE, prec = TRUE)

Plot change year density

Description

The function extracts change years from results ofgr_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# separatesep = gr_separate(spas, params = gr_get_params(reg = 'center'))# summarize from 1965 to 1990vars = gr_summarize(sep, 1965, 1990)# test all variablestests = gr_test_vars(vars)# plot change year from Pettitt testgr_plot_tests(tests, type = 'year')

Plot interannual hydrograph variable changes

Description

This function plots the hydrograph separation variables produced bygr_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 ofggplot2 objects, one for each variable, representing its interannual changes

Examples

library(grwat)data(spas) # example Spas-Zagorye data is included with grwat package# separatesep = gr_separate(spas, params = gr_get_params(reg = 'center'))# summarize from 1965 to 1990vars = gr_summarize(sep, 1965, 1990)# plot one selected variablegr_plot_vars(vars, Qygr) # plot two variables sequentiallygr_plot_vars(vars, D10w1, Wsprngr)# four variables in matrix layout with tests calculated on the flygr_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) fromgrwat reanalysis for using withgr_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 fromhere.

Value

list containing time series, precipitation series, temperature seriesand 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 examplereport generated by this command forspas 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

Adata.frame with 11 columns:

Examples

library(grwat)data(spas) # example Spas-Zagorye data is included with grwat packagehead(spas)# separatesep = gr_separate(spas, params = gr_get_params(reg = 'center'))# Visualizegr_plot_sep(sep, c(1978, 1989)) # Debug mode gives access to additional informationsep_debug = gr_separate(spas,                         params = gr_get_params(reg = 'center'),                         debug = TRUE)# a vector of years with jittered paramsjit = attributes(sep_debug)$jitteredprint(jit)# actual params used for each yearparlist = attributes(sep_debug)$params# tabular representation of parameterspartab = gr_to_pardf(parlist)head(partab)parlist2 = partab |>   dplyr::select(-year) |>   apply(1, as.list) |>   lapply(\(X) {    n = length(X)    X[1:(n - 1)] <- lapply(X[1:(n - 1)], as.numeric)    return(X)  }) |>   setNames(partab$year)# extract and tweak parameters for selected yearp = parlist[['1989']]p$grad1 = 1p$grad2 = 2.5# use tweaked parameters for all yearssep_debug = gr_separate(spas, params = p, debug = TRUE)# Visualizegr_plot_sep(sep_debug, c(1978, 1989)) # actual params used for each yearparlist = attributes(sep_debug)$params# tweak parameters for selected yearparlist[['1989']]$grad1 = 3parlist[['1989']]$grad2 = 6# set the sprecdays parameter for multiple yearsparlist = gr_set_param(parlist, sprecdays,                        years = c(1978, 1989:1995),                        value = 15)# set the spcomp parameter for all yearsparlist = gr_set_param(parlist, spcomp, value = 2.5)# use the list of parameters for separationsep_debug = gr_separate(spas, params = parlist, debug = TRUE)# Visualizegr_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 isru_RU.UTF-8, and Ukrainian locale isuk_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# separatesep = gr_separate(spas, params = gr_get_params(reg = 'center'))# Default is  Englishgr_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 bygr_separate() withdebug = TRUE set.

Usage

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

Arguments

Value

list oflists — a modified version ofparams

Examples

library(grwat)data(spas) # example Spas-Zagorye data is included with grwat package# Debug mode gives access to additional informationsep = gr_separate(spas,                   params = gr_get_params(reg = 'center'),                   debug = TRUE)# Visualizegr_plot_sep(sep, c(1978, 1989)) # actual params used for each yearparlist = attributes(sep)$params# set the sprecdays parameter for multiple yearsparlist = gr_set_param(parlist, sprecdays,                        years = c(1978, 1989:1995),                        value = 15)# use the list of parameters for separationsep_new = gr_separate(spas, params = parlist, debug = TRUE)# Visualizegr_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 bygr_help_vars(). This function is a convenient wrapper arounddplyr's⁠df |> 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 bygr_help_vars().

Examples

library(grwat)data(spas) # example Spas-Zagorye data is included with grwat package# separatesep = gr_separate(spas, params = gr_get_params(reg = 'center'))# summarizevars = 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 bygr_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# separatesep = gr_separate(spas, params = gr_get_params(reg = 'center'))# summarize from 1965 to 1990vars = gr_summarize(sep, 1965, 1990)# test all variablestests = gr_test_vars(vars)# view Pettitt test for Qygrtests$ptt$Qygr# view Fisher test for Q30stests$ft$Q30s# test only Qygr and Q30s using 1978 as fixed year and excluding 1988-1991 yrsgr_test_vars(vars, Qygr, Q30s, year = 1978, exclude = 1981:1983)

Convert list of parameters to data frame

Description

Convert list of parameters to data frame

Usage

gr_to_pardf(params)

Arguments

Value

tibble data frame with tabular representation of hydrograph separation parameters

Examples

library(grwat)data(spas) # example Spas-Zagorye data is included with grwat packagehead(spas)# separatesep = gr_separate(spas, params = gr_get_params(reg = 'center'))# Visualizegr_plot_sep(sep, c(1978, 1989)) # Debug mode gives access to additional informationsep_debug = gr_separate(spas,                         params = gr_get_params(reg = 'center'),                         debug = TRUE)# a vector of years with jittered paramsjit = attributes(sep_debug)$jitteredprint(jit)# actual params used for each yearparlist = attributes(sep_debug)$params# tweak parameters for selected yearparlist[['1989']]$grad1 = 3parlist[['1989']]$grad2 = 6# tabular representation of parameterspartab = gr_to_pardf(parlist)head(partab)partab |>   dplyr::filter(year == 1989) |>   head()

Spas-Zagorye daily runoff data

Description

A dataset containing the daily runoff data forSpas-Zagorye gauge onProtva 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