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Advanced hydrograph separation

Source: R/separate.R
gr_separate.Rd

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

Usage

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

Arguments

df

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

params

list of separation parameters, as returned by gr_get_params() function. Can also be a list of such lists if modified parameters are required for some years. In this case the length of params must be equal to the number of calendar years in df or be equal to 1.

debug

Boolean. If TRUE then additional attributes jittered and params are written to the output data.frame. jittered is an integer vector of years for which the separation parameters were randomly jittered. params is a list of separation parameter lists used for each year (some o those may have been jittered). Defaults to FALSE.

Value

A data.frame with 11 columns:

ColumnDescription
Datedate
Qtotal runoff
Temptemperature
Precprecipitation
Qbasebaseflow
Quickquickflow
Qsprispring flood
Qrainrain floods
Qthawthaw floods
Seasona season of the year
Yeara water-resources year

Examples

library(grwat)

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

# separate
sep = gr_separate(spas, params = gr_get_params(reg = 'center'))
#> grwat: data frame is correct
#> grwat: parameters list and types are OK

# 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)
#> grwat: data frame is correct
#> grwat: parameters list and types are OK
#> Warning: grwat: 1974 years were not separated. Check the input data for possible errors. Use gr_get_gaps() and gr_fill_gaps() functions to detect and fill missing data.
#> Warning: grwat: 2002, 2014, 2019 years were processed with jittered parameters

# a vector of years with jittered params
jit = attributes(sep_debug)$jittered
print(jit)
#> [1] 2002 2014 2019

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

# tabular representation of parameters
partab = gr_to_pardf(parlist)
head(partab)
#> # A tibble: 6 × 40
#>    year winmon grad1 grad2 gratio spmon1 spmon2 sprisedays1 sprisedays2 spdays
#>   <int>  <dbl> <dbl> <dbl>  <dbl>  <dbl>  <dbl>       <dbl>       <dbl>  <dbl>
#> 1  1956     11   1.7     5    400      2      5           8          10     30
#> 2  1957     11   1.7     5    400      2      5           8          10     30
#> 3  1958     11   1.7     5    400      2      5           8          10     30
#> 4  1959     11   1.7     5    400      2      5           8          10     30
#> 5  1960     11   1.7     5    400      2      5           8          10     30
#> 6  1961     11   1.7     5    400      2      5           8          10     30
#> # ℹ 30 more variables: sprise <dbl>, spratio <dbl>, sprecdays <dbl>,
#> #   spcomp <dbl>, precdays <dbl>, frostdays <dbl>, windays <dbl>,
#> #   floodprec <dbl>, floodtemp <dbl>, frosttemp <dbl>, wintemp <dbl>,
#> #   signratio1 <dbl>, signratio2 <dbl>, floodratio <dbl>, gaplen <dbl>,
#> #   snowtemp <dbl>, gradabs <dbl>, mntmode <dbl>, mntgrad <dbl>,
#> #   mntavgdays <dbl>, mntratiodays <dbl>, mntratio <dbl>, niter <dbl>, a <dbl>,
#> #   k <dbl>, C <dbl>, aq <dbl>, padding <dbl>, passes <dbl>, filter <chr>

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 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)
#> grwat: data frame is correct
#> grwat: parameters list and types are OK
#> Warning: grwat: 1974 years were not separated. Check the input data for possible errors. Use gr_get_gaps() and gr_fill_gaps() functions to detect and fill missing data.
#> Warning: grwat: 2002, 2014, 2019 years were processed with jittered parameters

# 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)
#> grwat: data frame is correct
#> grwat: parameters list and types are OK
#> Warning: grwat: 1974 years were not separated. Check the input data for possible errors. Use gr_get_gaps() and gr_fill_gaps() functions to detect and fill missing data.

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