Identify typical water management schedule across multiple water years of data
Arguments
- df
Input tibble from
estimate_wetland_mode()- fullmode
One of
MorH; only necessary ifclean = TRUE; see Details- clean
Logical; determines whether to correct for repeat values of F and D
Value
Input tibble with only one entry for each month for each unit, the
most frequent value for "mode", and an additional field weight giving the
proportion of years observed containing that management mode in that month.
Details
For each unit, identifies and returns the most frequent management
mode in each month and calculates weight as the proportion of water years
in which that mode was detected. Low values for weight reflect months with
more annual variability in the monthly management schedules.
Note that ties are not returned, and in the case of ties, the function
favors returning modes in the following order: F, D, I, M, H, N (see
estimate_wetland_mode() for details). It is therefore possible for the
function to return multiple months with values for F and D. Setting
clean=TRUE will reclassify duplicate values of F and D to fullmode, but
will also remove weight estimates since they may no longer be valid.
Examples
df = format_watertracker(sampledat) |> estimate_flood_extent() |>
estimate_flood_delta() |> estimate_wetland_mode()
generalize_wetland_mode(df)
#> # A tibble: 204 × 10
#> WETLAND unit CLASS AREA_HA AREA_AC AREA_AC_WETTED month_name month mode
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <fct> <dbl> <fct>
#> 1 SampleWetl… Samp… mana… 37.1 91.6 61.2 Oct 10 F
#> 2 SampleWetl… Samp… mana… 37.1 91.6 61.2 Nov 11 M
#> 3 SampleWetl… Samp… mana… 37.1 91.6 61.2 Dec 12 M
#> 4 SampleWetl… Samp… mana… 37.1 91.6 61.2 Jan 1 M
#> 5 SampleWetl… Samp… mana… 37.1 91.6 61.2 Feb 2 M
#> 6 SampleWetl… Samp… mana… 37.1 91.6 61.2 Mar 3 D
#> 7 SampleWetl… Samp… mana… 37.1 91.6 61.2 Apr 4 D
#> 8 SampleWetl… Samp… mana… 37.1 91.6 61.2 May 5 N
#> 9 SampleWetl… Samp… mana… 37.1 91.6 61.2 Jun 6 N
#> 10 SampleWetl… Samp… mana… 37.1 91.6 61.2 Jul 7 N
#> # ℹ 194 more rows
#> # ℹ 1 more variable: weight <dbl>