Extract baseflow

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

Q

Numeric runoff vector.

a

Numeric value of a filtering parameter used in 'chapman', 'jakeman' and 'lynehollick' methods. Defaults to 0.925.

k

Numeric value of a filtering parameter used in 'boughton' and 'maxwell' methods. Defaults to 0.975.

C

Numeric value of a separation shape parameter used in 'boughton', 'jakeman' and 'maxwell' methods

aq

Numeric value of a filtering parameter used in 'jakeman' method. Defaults to -0.5.

passes

Integer number of filtering iterations. The first iteration is forward, second is backward, third is forward and so on. Defaults to 3.

padding

Integer number of elements padded at the beginning and ending of runoff vector to reduce boundary effects. Defaults to 30.

method

Character string to set baseflow filtering method. Available methods are 'boughton', 'chapman', 'furey', 'jakeman', 'lynehollick' and 'maxwell'. Default is 'lynehollick', which corresponds to Lyne-Hollick (1979) hydrograph separation method.

Value

Numeric baseflow vector with length equal to Q

Examples

library(grwat)
library(ggplot2)
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
library(tidyr)
library(lubridate)
#> 
#> Attaching package: ‘lubridate’
#> The following objects are masked from ‘package:base’:
#> 
#>     date, intersect, setdiff, union

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)))
#> Warning: Removed 23376 rows containing non-finite values (`stat_align()`).
#> Warning: Removed 23376 rows containing non-finite values (`stat_align()`).


# 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)
#> Warning: Removed 93508 rows containing non-finite values (`stat_align()`).
#> Warning: Removed 93508 rows containing non-finite values (`stat_align()`).


# 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')
#> grwat: data frame is correct
#> grwat: parameters list and types are OK

# 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)
#> Warning: Removed 46752 rows containing non-finite values (`stat_align()`).
#> Warning: Removed 46752 rows containing non-finite values (`stat_align()`).