Simulate Performance of LID

Usage

simulate_performance(
  lid_selected,
  lid_area_fraction = 0,
  catchment_area_m2 = 1000,
  swmm_base_inp = keys.lid::extdata_file("scenarios/models/model_template.inp"),
  swmm_climate_dir = keys.lid::extdata_file("rawdata/weather_sponge_regions"),
  swmm_exe = NULL,
  model_dir = keys.lid::extdata_file("scenarios/models"),
  zone_ids = 1L:5L
)

Arguments

lid_selected: tibble with a selected LID as retrieved by read_scenarios
lid_area_fraction: fraction of LID in subcatchment (default: 0)
catchment_area_m2: catchment area (default: 1000 m2)
swmm_base_inp: path to SWMM model to be used as template for modification (default: keys.lid::extdata_file("scenarios/models/model_template.inp"))
swmm_climate_dir: directory with climate data (default: keys.lid::extdata_file("rawdata/weather_sponge_regions")
swmm_exe: Name and path to swmm5 executable. If not manually set, the following paths are looked up: linux: "/usr/bin/swmm5" darwin: "/Applications/swmm5" windows: "C:/Program Files (x86)/EPA SWMM 5.1/swmm5.exe", (default: NULL)
model_dir: default: keys.lid::extdata_file("scenarios/models")
zone_ids: climate zone ids to be used for simulation (default: 1L:5L)

Value

tibble with nested lists containing all scenario performance

Examples

if (FALSE) {
scenarios <- keys.lid::read_scenarios()
unique(scenarios$lid_name_tidy)
lid <- "permeable_pavement"
lid_selected <- scenarios %>%  dplyr::filter(.data$lid_name_tidy == lid)
pp_0.00 <- keys.lid::simulate_performance(lid_selected,
                                          lid_area_fraction = 0.00)
pp_1.0 <- keys.lid::simulate_performance(lid_selected,
                                         lid_area_fraction = 1.0)
pp <- dplyr::bind_rows(pp_0.00, pp_1.0)
}