diff --git a/.Rbuildignore b/.Rbuildignore
index 6f4318f..a038a86 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -8,3 +8,4 @@
 ^codecov\.yml$
 ^index\.md$
 ^README\.md$
+^data-raw$
diff --git a/DESCRIPTION b/DESCRIPTION
index 698a0b0..fdb3c84 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -24,15 +24,28 @@ Description: R Package for Simulating the Impact of Different
 License: MIT + file LICENSE
 URL: https://github.com/KWB-R/keys.lid
 BugReports: https://github.com/KWB-R/keys.lid/issues
+Depends: 
+    R (>= 2.10)
 Imports: 
     dplyr,
+    ggplot2,
+    lubridate,
     kwb.event,
     kwb.swmm,
+    kwb.utils,
+    plotly,
+    scales,
+    stringr,
     swmmr,
+    readr,
     readxl,
+    rlang,
+    tibble,
     tidyr,
+    tidyselect,
     xts,
-    zoo
+    zoo,
+    magrittr
 Suggests: 
     car,
     covr,
@@ -44,8 +57,10 @@ VignetteBuilder:
     knitr
 Remotes:
     github::kwb-r/kwb.event,
-    github::kwb-r/kwb.swmm
+    github::kwb-r/kwb.swmm,
+    github::kwb-r/kwb.utils
 Encoding: UTF-8
 LazyData: true
+LazyDataCompression: xz
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.1.1
diff --git a/NAMESPACE b/NAMESPACE
index 19e62a7..98529d9 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,22 +1,64 @@
 # Generated by roxygen2: do not edit by hand
 
+export("%>%")
+export(boxplot_runoff_max)
+export(boxplot_runoff_volume)
+export(boxplot_vrr)
 export(computeVol)
 export(extdata_file)
+export(lidconfig_to_swmm)
 export(makeRainfallRunoffEvents)
 export(monthlyPattern)
+export(plot_vrr_median)
 export(readObservations)
 export(readPredictions)
 export(read_scenarios)
+export(simulate_performance)
+export(simulate_performances)
+import(ggplot2)
+importFrom(dplyr,any_vars)
+importFrom(dplyr,arrange)
 importFrom(dplyr,bind_rows)
+importFrom(dplyr,desc)
+importFrom(dplyr,filter_at)
+importFrom(dplyr,group_by)
 importFrom(dplyr,left_join)
+importFrom(dplyr,mutate)
+importFrom(dplyr,select)
+importFrom(dplyr,summarise)
+importFrom(dplyr,vars)
 importFrom(kwb.event,getEvents)
+importFrom(kwb.swmm,calculate_rainevent_stats)
+importFrom(kwb.swmm,extdata_file)
+importFrom(kwb.swmm,get_results)
+importFrom(kwb.utils,catAndRun)
+importFrom(kwb.utils,resolve)
+importFrom(lubridate,year)
+importFrom(magrittr,"%>%")
+importFrom(plotly,ggplotly)
+importFrom(plotly,layout)
+importFrom(plotly,plot_ly)
+importFrom(readr,cols)
+importFrom(readr,read_csv)
 importFrom(readxl,excel_sheets)
 importFrom(readxl,read_xlsx)
+importFrom(rlang,.data)
 importFrom(stats,aggregate)
 importFrom(stats,approx)
+importFrom(stats,median)
 importFrom(stats,setNames)
+importFrom(stringr,str_replace)
+importFrom(stringr,str_to_upper)
+importFrom(swmmr,read_inp)
 importFrom(swmmr,read_out)
+importFrom(swmmr,run_swmm)
+importFrom(swmmr,write_inp)
+importFrom(tibble,tibble)
 importFrom(tidyr,pivot_longer)
+importFrom(tidyr,pivot_wider)
+importFrom(tidyr,unnest)
+importFrom(tidyselect,all_of)
+importFrom(tidyselect,starts_with)
 importFrom(utils,read.table)
 importFrom(xts,tzone)
 importFrom(zoo,coredata)
diff --git a/R/lidconfig_to_swmm.R b/R/lidconfig_to_swmm.R
new file mode 100644
index 0000000..7d049ae
--- /dev/null
+++ b/R/lidconfig_to_swmm.R
@@ -0,0 +1,56 @@
+#' Convert LID config to SWMM LID controls
+#' @param df data frame for a single scenario of a LID (as returned by
+#' \code{\link{read_scenarios}})
+#' @return data frame with SWMM LID controls
+#' @export
+#' @importFrom readr cols read_csv
+#' @importFrom kwb.swmm extdata_file
+#' @importFrom dplyr any_vars bind_rows filter_at left_join mutate select vars
+#' @importFrom tidyr pivot_wider
+#' @importFrom rlang .data
+#' @importFrom tidyselect all_of starts_with
+#' @importFrom stringr str_to_upper
+#' @examples
+#' scenarios <- keys.lid::read_scenarios()
+#' unique(scenarios$lid_name_tidy)
+#' lid <- "permeable_pavement"
+#' lid_selected <- scenarios %>%  dplyr::filter(.data$lid_name_tidy == lid)
+#' scenario_names <- unique(lid_selected$scenario_name)
+#' scenario_name <- scenario_names[1]
+#' scenario_name
+#' lid_selected_scenario <- lid_selected[lid_selected$scenario_name == scenario_name,]
+#' lid_controls <- lidconfig_to_swmm(lid_selected_scenario)
+#' str(lid_controls)
+lidconfig_to_swmm <- function(df) {
+
+  lid_para <- readr::read_csv(kwb.swmm::extdata_file("lid/required_parameteristion.csv"),
+                              col_types = readr::cols(.default = "c"))
+
+  lid_parametersation <- df %>%
+    dplyr::filter(!is.na(.data$value)) %>%
+    dplyr::filter(!is.na(.data$id_type_parameter)) %>%
+    dplyr::select(tidyselect::all_of(c("lid_name_tidy", "type", "id_type_parameter", "scenario_name", "value"))) %>%
+    dplyr::left_join(lid_para  %>%
+                       dplyr::select(.data$lid_id, .data$lid_name_tidy), by = "lid_name_tidy") %>%
+    dplyr::mutate("Name" = sprintf("%s.%s", .data$lid_name_tidy, .data$scenario_name),
+                  "Type/Layer" =  stringr::str_to_upper(.data$type),
+    ) %>%
+    dplyr::filter(!is.na(.data$value)) %>%
+    dplyr::select(tidyselect::all_of(c("Name", "Type/Layer", "id_type_parameter", "value"))) %>%
+    tidyr::pivot_wider(names_from = "id_type_parameter",
+                       names_prefix = "Par",
+                       values_from = "value") %>%
+    dplyr::filter_at(dplyr::vars(tidyselect::starts_with("Par")), dplyr::any_vars(!is.na(.data)))
+
+  ## dont know why 5 is needed by SWMM (but generated in SWMM GUI)
+  lid_parametersation[lid_parametersation$`Type/Layer` == "SURFACE", "Par5"] <- 5
+
+  lid_id <- lid_para$lid_id[lid_para$lid_name_tidy == unique(df$lid_name_tidy)]
+
+  lid_header <-   lid_parametersation[1,]
+  lid_header[1,3:ncol(lid_header)] <- NA_real_
+  lid_header$`Type/Layer` <- lid_id
+
+  dplyr::bind_rows(lid_header, lid_parametersation)
+
+}
diff --git a/R/performances.R b/R/performances.R
new file mode 100644
index 0000000..8def19a
--- /dev/null
+++ b/R/performances.R
@@ -0,0 +1,25 @@
+#' Performance results for LIDs
+#'
+#' A dataset containing the performance of LIDs for different climate conditions
+#' created with R script in /data-raw/performances.R
+#'
+#' @format A nested tibble with 290 rows and 16 variables:
+#' \describe{
+#'   \item{zone_id}{climate zone id}
+#'   \item{lid_name_tidy}{tidy LID name}
+#'   \item{scenario_name}{name of LID scenario}
+#'   \item{catchment_area_m2}{catchment area in squaremeters}
+#'   \item{lid_area_fraction}{fraction of LID compared to total catchment}
+#'   \item{lid_area_m2}{total LID area}
+#'   \item{lid_usage}{tibble with LID usage parameterisation}
+#'   \item{lid_controls}{tibble with LID controls parameterisation}
+#'   \item{subcatchment}{tibble with subcatchment parameterisation}
+#'   \item{annual}{tibble with two columns "year" and "vrr" (volume rainfall retended for each year}
+#'   \item{events_max}{tibble with maximum values for each rainfall event}
+#'   \item{events_sum}{tibble with sum values for each rainfall event}
+#'   \item{col_eventsep}{name of SWMM results used for event separation}
+#'   \item{model_inp}{path to SWMM model input file}
+#'   \item{model_rpt}{path to SWMM model report file}
+#'   \item{model_out}{path to SWMM model output file}
+#' }
+"performances"
diff --git a/R/plot.R b/R/plot.R
new file mode 100644
index 0000000..c0b6d8a
--- /dev/null
+++ b/R/plot.R
@@ -0,0 +1,212 @@
+#' Plot Median VRR
+#'
+#' @param lid tidy name of LID
+#' @param performances nested tibble (default: \code{\link{performances}})
+#' @return interactive plot of performance results
+#' @export
+#' @importFrom tidyr unnest
+#' @importFrom dplyr group_by summarise
+#' @importFrom plotly ggplotly layout
+#' @import ggplot2
+#' @importFrom stats median
+#' @examples
+#' \dontrun{
+#' lids <- unique(keys.lid::performances$lid_name_tidy)
+#' sapply(lids, function(lid) print(keys.lid::plot_vrr_median(lid)))
+#' }
+plot_vrr_median <- function(lid = "bioretention_cell",
+                            performances = keys.lid::performances) {
+
+  perf_selected <- performances %>%
+    dplyr::filter(.data$lid_name_tidy == lid)
+
+  catchment_area_m2 <- unique(perf_selected$catchment_area_m2)
+
+  g <- perf_selected %>%
+    dplyr::mutate(label = sprintf("%s (%d m2)", .data$scenario_name, .data$lid_area_m2)) %>%
+    tidyr::unnest(.data$annual) %>%
+    # dplyr::filter(.data$vrr > 0) %>%
+    dplyr::group_by(.data$zone_id,
+                    .data$lid_name_tidy,
+                    .data$scenario_name,
+                    .data$lid_area_fraction) %>%
+    dplyr::summarise(vrr_median = stats::median(.data$vrr)) %>%
+    dplyr::ungroup() %>%
+    ggplot2::ggplot(ggplot2::aes_string(x = "lid_area_fraction",
+                                        y = "vrr_median",
+                                        color = "scenario_name")) +
+    ggplot2::facet_wrap(~ zone_id, ncol = 1) +
+    ggplot2::geom_line() +
+    ggplot2::geom_point() +
+    ggplot2::labs(title = sprintf("%s (catchment area: %d m2)",
+                                  lid,
+                                  catchment_area_m2),
+                  y = "Median Volume Rainfall Retended per Year (%)") +
+    ggplot2::coord_cartesian(ylim = c(0,1)) +
+    ggplot2::scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
+    ggplot2::theme_bw() +
+    ggplot2::theme(legend.position = "bottom")
+
+  plotly::ggplotly(g) %>%
+    plotly::layout(legend = list(orientation = "h", x = 0, y = -0.1 ),
+                   ylab = list(orientation = "v", x = 0, y = -0.4 ))
+}
+
+#' Boxplot Volume Rainfall Retended per Year
+#'
+#' @param lid tidy name of LID (default: "bioretention_cell")
+#' @param zone_id climate zone id to plot (default: 1)
+#' @param performances nested tibble (default: \code{\link{performances}})
+#' @return interactive plot of performance results
+#' @export
+#' @importFrom tidyr unnest
+#' @importFrom dplyr group_by summarise
+#' @importFrom plotly plot_ly layout
+#' @examples
+#' \dontrun{
+#' boxplot_vrr(lid = "bioretention_cell", zone_id = 1)
+#' }
+boxplot_vrr <- function(lid = "bioretention_cell",
+                        zone_id = 1,
+                        performances = keys.lid::performances) {
+
+  sel_zone <- as.integer(zone_id)
+
+  perf_selected <- performances %>%
+    dplyr::mutate(zone_id = as.integer(.data$zone_id)) %>%
+    dplyr::filter(.data$lid_name_tidy == lid,
+                  .data$zone_id == sel_zone)
+
+  catchment_area_m2 <- unique(perf_selected$catchment_area_m2)
+
+  perf_selected %>%
+    dplyr::mutate(lid_area_fraction = as.factor(.data$lid_area_fraction),
+                  scenario_name = as.factor(.data$scenario_name),
+                  label = sprintf("%s (%d m2)", .data$scenario_name, .data$lid_area_m2)) %>%
+    tidyr::unnest(.data$annual) %>%
+    dplyr::group_by(.data$zone_id,
+                    .data$lid_name_tidy,
+                    .data$scenario_name,
+                    .data$lid_area_fraction) %>%
+    plotly::plot_ly(x = ~lid_area_fraction,
+                    y = ~vrr*100,
+                    color = ~scenario_name,
+                    type = "box") %>%
+    plotly::layout(boxmode = "group",
+                   title = sprintf("zone %d: %s (catchment area: %s m2)",
+                                   zone_id,
+                                   lid,
+                                   catchment_area_m2),
+                   xaxis = list(title='LID area fraction'),
+                   yaxis = list(title='Volume Rainfall Retended (%)',
+                                range = c(0, 100)),
+                   legend = list(orientation = "h", x = 0, y = -0.1 ))
+
+
+}
+
+
+#' Boxplot Runoff Maximum per Event
+#'
+#' @param lid tidy name of LID (default: "bioretention_cell")
+#' @param zone_id climate zone id to plot (default: 1)
+#' @param performances nested tibble (default: \code{\link{performances}})
+#' @return interactive plot of performance results
+#' @export
+#' @importFrom tidyr unnest
+#' @importFrom dplyr group_by summarise
+#' @importFrom plotly plot_ly layout
+#' @examples
+#' \dontrun{
+#' boxplot_runoff_max(lid = "bioretention_cell", zone_id = 1)
+#' }
+boxplot_runoff_max <- function(lid = "bioretention_cell",
+                               zone_id = 1,
+                               performances = keys.lid::performances) {
+
+  sel_zone <- as.integer(zone_id)
+
+  perf_selected <- performances %>%
+    dplyr::mutate(zone_id = as.integer(.data$zone_id)) %>%
+    dplyr::filter(.data$lid_name_tidy == lid,
+                  .data$zone_id == sel_zone)
+
+  catchment_area_m2 <- unique(perf_selected$catchment_area_m2)
+
+  perf_selected %>%
+    dplyr::mutate(lid_area_fraction = as.factor(lid_area_fraction),
+                  scenario_name = as.factor(.data$scenario_name),
+                  label = sprintf("%s (%d m2)", .data$scenario_name, .data$lid_area_m2)) %>%
+    tidyr::unnest(.data$events_max) %>%
+    dplyr::group_by(.data$zone_id,
+                    .data$lid_name_tidy,
+                    .data$scenario_name,
+                    .data$lid_area_fraction) %>%
+    plotly::plot_ly(x = ~lid_area_fraction,
+                    y = ~max_total_runoff_mmPerHour,
+                    color = ~scenario_name,
+                    type = "box") %>%
+    plotly::layout(boxmode = "group",
+                   title = sprintf("zone %d: %s (catchment area: %s m2)",
+                                   zone_id,
+                                   lid,
+                                   catchment_area_m2),
+                   xaxis = list(title='LID area fraction'),
+                   yaxis = list(title='Maximum total runoff (mm/h per event)'),
+                   legend = list(orientation = "h", x = 0, y = -0.1 ))
+
+
+}
+
+#' Boxplot Runoff Volume per Event
+#'
+#' @param lid tidy name of LID (default: "bioretention_cell")
+#' @param zone_id climate zone id to plot (default: 1)
+#' @param performances nested tibble (default: \code{\link{performances}})
+#' @return interactive plot of performance results
+#' @export
+#' @importFrom tidyr unnest
+#' @importFrom dplyr group_by summarise
+#' @importFrom plotly plot_ly layout
+#' @examples
+#' \dontrun{
+#' boxplot_runoff_volume(lid = "bioretention_cell", zone_id = 1)
+#' }
+boxplot_runoff_volume <- function(lid = "bioretention_cell",
+                               zone_id = 1,
+                               performances = keys.lid::performances) {
+
+  sel_zone <- as.integer(zone_id)
+
+  perf_selected <- performances %>%
+    dplyr::mutate(zone_id = as.integer(.data$zone_id)) %>%
+    dplyr::filter(.data$lid_name_tidy == lid,
+                  .data$zone_id == sel_zone)
+
+  catchment_area_m2 <- unique(perf_selected$catchment_area_m2)
+
+  perf_selected %>%
+    dplyr::mutate(lid_area_fraction = as.factor(lid_area_fraction),
+                  scenario_name = as.factor(.data$scenario_name),
+                  label = sprintf("%s (%d m2)", .data$scenario_name, .data$lid_area_m2)) %>%
+    tidyr::unnest(.data$events_sum) %>%
+    dplyr::group_by(.data$zone_id,
+                    .data$lid_name_tidy,
+                    .data$scenario_name,
+                    .data$lid_area_fraction) %>%
+    dplyr::mutate(sum_total_runoff_cbm = .data$dur * .data$sum_total_runoff / 1000 / catchment_area_m2) %>%
+    plotly::plot_ly(x = ~lid_area_fraction,
+                    y = ~sum_total_runoff_cbm,
+                    color = ~scenario_name,
+                    type = "box") %>%
+    plotly::layout(boxmode = "group",
+                   title = sprintf("zone %d: %s (catchment area: %s m2)",
+                                   zone_id,
+                                   lid,
+                                   catchment_area_m2),
+                   xaxis = list(title='LID area fraction'),
+                   yaxis = list(title='Total Runoff Volume (m3 per m2 per event)'),
+                   legend = list(orientation = "h", x = 0, y = -0.1 ))
+
+
+}
diff --git a/R/read_scenarios.R b/R/read_scenarios.R
index a43699f..d604120 100644
--- a/R/read_scenarios.R
+++ b/R/read_scenarios.R
@@ -9,25 +9,28 @@
 #' @importFrom readxl excel_sheets read_xlsx
 #' @importFrom stats setNames
 #' @importFrom tidyr pivot_longer
+#' @importFrom tidyselect all_of
 #'
 read_scenarios <- function(
   scenarios_xlsx = extdata_file("scenarios/swmm_lid-parameterisation.xlsx")
 ) {
-sheets <- readxl::excel_sheets(scenarios_xlsx)
+lids <- readxl::excel_sheets(scenarios_xlsx)
 
 scenarios <- dplyr::bind_rows(stats::setNames(
-  lapply(sheets, function(sheet) {
-  readxl::read_xlsx(scenarios_xlsx, sheet = sheet)
-}), nm = sheets),
+  lapply(lids, function(lid) {
+  lid <- readxl::read_xlsx(scenarios_xlsx, sheet = lid)
+  cols <- names(lid)[!names(lid) %in% c("lid_name_tidy",
+                                                    "type",
+                                                    "id_type_parameter",
+                                                    "parameter_unit",
+                                                    "comment",
+                                                    "reference")]
+  tidyr::pivot_longer(lid,
+                      cols = tidyselect::all_of(cols),
+                      names_to = "scenario_name",
+                      values_to = "value")
+}), nm = lids),
 .id = "lid_name_tidy")
 
-cols <- names(scenarios)[!names(scenarios) %in% c("lid_name_tidy",
-                                                  "type",
-                                                  "parameter_unit",
-                                                  "comment",
-                                                  "reference")]
-tidyr::pivot_longer(scenarios,
-                    cols = cols,
-                    names_to = "scenario_name",
-                    values_to = "value")
+
 }
diff --git a/R/simulate_performance.R b/R/simulate_performance.R
new file mode 100644
index 0000000..76f9701
--- /dev/null
+++ b/R/simulate_performance.R
@@ -0,0 +1,189 @@
+#' Simulate Performance of LID
+#'
+#' @param lid_selected tibble with a selected LID as retrieved by \code{\link{read_scenarios}}
+#' @param lid_area_fraction fraction of LID in subcatchment (default: 0)
+#' @param catchment_area_m2 catchment area (default: 1000 m2)
+#' @param col_eventsep SWMM output column used for event separation (default:
+#' "total_rainfall")
+#' @param swmm_base_inp path to SWMM model to be used as template for modification
+#' (default: keys.lid::extdata_file("scenarios/models/model_template.inp"))
+#' @param swmm_climate_dir directory with climate data
+#' (default: keys.lid::extdata_file("rawdata/weather_sponge_regions")
+#' @param 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)
+#' @param model_dir default:  keys.lid::extdata_file("scenarios/models")
+#' @param zone_ids climate zone ids to be used for simulation (default: 1L:5L)
+
+#' @return tibble with nested lists containing all scenario performance
+#' @importFrom dplyr arrange desc
+#' @importFrom rlang .data
+#' @importFrom lubridate year
+#' @importFrom tibble tibble
+#' @importFrom stringr str_replace
+#' @importFrom swmmr read_inp run_swmm write_inp
+#' @importFrom kwb.swmm calculate_rainevent_stats get_results
+#' @importFrom kwb.utils resolve catAndRun
+#' @export
+#' @examples
+#' \dontrun{
+#' 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)
+#' }
+simulate_performance <- function(
+  lid_selected,
+  lid_area_fraction = 0,
+  catchment_area_m2 = 1000,
+  col_eventsep = "total_rainfall",
+  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
+) {
+
+  swmm_inp <- swmmr::read_inp(swmm_base_inp)
+
+  flow_unit <- swmm_inp$options$Value[swmm_inp$options$Option == "FLOW_UNITS"]
+  stopifnot(flow_unit == "LPS")
+
+  lid_area_m2 <- lid_area_fraction * catchment_area_m2
+
+  lapply(zone_ids, function(zone_id) {
+  scenario_names <- unique(lid_selected$scenario_name)
+  msg_txt <- sprintf("Simulating LID '%s' with %s scenarios for climate zone %d (remaining zones: %d)",
+                     unique(lid_selected$lid_name_tidy),
+                     length(scenario_names),
+                     zone_id, length(zone_ids)-zone_id)
+  kwb.utils::catAndRun(messageText = msg_txt, expr = {
+  lapply(scenario_names, function(selected_scenario) {
+    lid_selected_scenario <- lid_selected %>%
+      dplyr::filter(.data$scenario_name == selected_scenario)
+
+    lid_controls <- lidconfig_to_swmm(lid_selected_scenario)
+
+
+    subcatchment <- tibble::tibble(Name = "S1",
+                                   `Rain Gage` = "RainGage",
+                                   Outlet = "Out1",
+                                   Area = kwb.swmm::squaremeter_to_hectar(catchment_area_m2),
+                                   Perc_Imperv = 100,
+                                   Width = 6,
+                                   Perc_Slope = 0.5,
+                                   Curb_Len = 0,
+                                   Snowpack = "snowPack1"
+    )
+
+    swmm_inp$subcatchments <- subcatchment
+
+
+    lid_usage <- tibble::tibble("Subcatchment" = "S1",
+                                "LID Process" = lid_controls$Name[1],
+                                "Number" = 1,
+                                "Area" = lid_area_m2,
+                                "Width" = 6,
+                                "InitSat" = 0,
+                                "FromImp" = 0,
+                                "ToPerv" = 0,
+                                "RptFile" = "*",
+                                "DrainTo" = "*                0"
+    )
+
+
+    path_inp_file <- paste0(sprintf("%s/zone-%d_%s_lidshare-%1.2f",
+                                    model_dir,
+                                    zone_id,
+                                    lid_controls$Name[1],
+                                    lid_area_fraction),
+                            ".inp")
+    path_rpt_file <- stringr::str_replace(path_inp_file, "\\.inp", "\\.rpt")
+    path_out_file <- stringr::str_replace(path_inp_file, "\\.inp", "\\.out")
+
+
+    swmm_inp$lid_controls <- lid_controls
+    swmm_inp$lid_usage  <- lid_usage # lid_controls$Name[1]
+
+
+    paths_list <- list(temp = "<climate_dir>/swmm_climeng_zone<zone_id>_temp.txt",
+                       rain = "<climate_dir>/swmm_bwsti_zone<zone_id>_rain_hourly.txt"
+                       )
+
+    paths <- kwb.utils::resolve(paths_list,
+                                climate_dir = swmm_climate_dir,
+                                zone_id = zone_id)
+
+    stopifnot(all(file.exists(unlist(paths)
+                              )
+                  )
+              )
+
+
+  swmm_inp$raingages$Source <- sprintf('FILE       \"%s\" BWSTI      MM',
+                                         normalizePath(paths$rain))
+  swmm_inp$temperature[swmm_inp$temperature$`Data Element`=="FILE", "Values"] <- sprintf('\"%s\"',
+                                                                                         normalizePath(paths$temp))
+
+  swmmr::write_inp(swmm_inp, file = path_inp_file)
+  swmmr::run_swmm(inp = path_inp_file,
+                  rpt = path_rpt_file,
+                  out = path_out_file,
+                  exec = swmm_exe
+                  )
+
+
+   lps_to_mmPerHour <- function(values) {
+     values * 3.6
+   }
+
+    results_system <- kwb.swmm::get_results(path_out = path_out_file,
+                                            vIndex = c(1,4)) %>%
+      dplyr::mutate(total_runoff_mmPerHour = lps_to_mmPerHour(.data$total_runoff))
+
+    results_vrr <-  results_system %>%
+      dplyr::mutate(year = lubridate::year(.data$datetime)) %>%
+      dplyr::group_by(.data$year) %>%
+      dplyr::summarise(vrr = 1 - (sum(.data$total_runoff_mmPerHour) / sum(.data$total_rainfall)))
+
+
+    rainevent_stats_sum <- kwb.swmm::calculate_rainevent_stats(results_system,
+                                                               col_eventsep = col_eventsep,
+                                                               aggregation_function = "sum") %>%
+      dplyr::arrange(dplyr::desc(.data$sum_total_rainfall))
+
+    rainevent_stats_max <- kwb.swmm::calculate_rainevent_stats(results_system,
+                                                               col_eventsep = col_eventsep,
+                                                               aggregation_function = "max") %>%
+      dplyr::arrange(dplyr::desc(.data$max_total_rainfall))
+
+
+    tibble::tibble(lid_name_tidy = unique(lid_selected$lid_name_tidy),
+                   scenario_name = selected_scenario,
+                   catchment_area_m2 = catchment_area_m2,
+                   lid_area_fraction = lid_area_fraction,
+                   lid_area_m2 = lid_area_m2,
+                   lid_usage = list(lid_usage),
+                   lid_controls = list(lid_controls),
+                   subcatchment = list(subcatchment),
+                   annual = list(results_vrr),
+                   events_sum = list(rainevent_stats_sum),
+                   events_max = list(rainevent_stats_max),
+                   col_eventsep = col_eventsep,
+                   model_inp = path_inp_file,
+                   model_rpt = path_rpt_file,
+                   model_out = path_out_file)
+
+  }) %>%
+    dplyr::bind_rows()
+
+})}) %>% dplyr::bind_rows(.id = "zone_id")
+
+
+}
diff --git a/R/simulate_performances.R b/R/simulate_performances.R
new file mode 100644
index 0000000..ff77249
--- /dev/null
+++ b/R/simulate_performances.R
@@ -0,0 +1,54 @@
+#' Simulate Performances of LID
+#'
+#' @param lid_area_fractions fractions of LID in subcatchment (default: c(0,1)
+#' @inheritParams simulate_performance
+#' @return tibble with nested lists containing all scenario performances for
+#' varying lid_area_fractions
+#' @export
+#' @importFrom dplyr arrange desc
+#' @importFrom rlang .data
+#' @importFrom lubridate year
+#' @importFrom tibble tibble
+#' @importFrom stringr str_replace
+#' @importFrom swmmr read_inp run_swmm write_inp
+#' @importFrom kwb.swmm calculate_rainevent_stats get_results
+#' @importFrom stats setNames
+#' @examples
+#' \dontrun{
+#' scenarios <- keys.lid::read_scenarios()
+#' unique(scenarios$lid_name_tidy)
+#' lid <- "permeable_pavement"
+#' lid_selected <- scenarios %>%  dplyr::filter(.data$lid_name_tidy == lid)
+#' pp <- keys.lid::simulate_performances(lid_selected,
+#'                                       lid_area_fractions = c(0,1)
+#'                                       )
+#' }
+simulate_performances <- function(
+  lid_selected,
+  lid_area_fractions = c(0,1),
+  catchment_area_m2 = 1000,
+  col_eventsep = "total_rainfall",
+  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
+) {
+    lid <- unique(lid_selected$lid_name_tidy)
+    label <- sprintf("%s_%0.2f", lid, lid_area_fractions)
+    stats::setNames(
+      lapply(lid_area_fractions, function(lid_area_fraction) {
+      simulate_performance(lid_selected,
+                           lid_area_fraction,
+                           catchment_area_m2,
+                           col_eventsep,
+                           swmm_base_inp,
+                           swmm_climate_dir,
+                           swmm_exe,
+                           model_dir,
+                           zone_ids)
+    }), nm = label) %>%
+      dplyr::bind_rows()
+}
+
+
diff --git a/R/utils-pipe.R b/R/utils-pipe.R
new file mode 100644
index 0000000..fd0b1d1
--- /dev/null
+++ b/R/utils-pipe.R
@@ -0,0 +1,14 @@
+#' Pipe operator
+#'
+#' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
+#'
+#' @name %>%
+#' @rdname pipe
+#' @keywords internal
+#' @export
+#' @importFrom magrittr %>%
+#' @usage lhs \%>\% rhs
+#' @param lhs A value or the magrittr placeholder.
+#' @param rhs A function call using the magrittr semantics.
+#' @return The result of calling `rhs(lhs)`.
+NULL
diff --git a/data-raw/performances.R b/data-raw/performances.R
new file mode 100644
index 0000000..cc14722
--- /dev/null
+++ b/data-raw/performances.R
@@ -0,0 +1,60 @@
+## code to prepare `DATASET` dataset goes here
+library(keys.lid)
+
+scenarios <- keys.lid::read_scenarios()
+
+paths_list <- list(
+  swmm_exe = "C:/Program Files (x86)/EPA SWMM 5.1.015/swmm5.exe"
+  )
+
+paths <- kwb.utils::resolve(paths_list)
+
+### takes about 2.5h for all four LIDs to simulate
+
+### Bioretention Cell
+br <- keys.lid::simulate_performances(
+  lid_selected = scenarios[scenarios$lid_name_tidy == "bioretention_cell",],
+  lid_area_fractions = c(0, 0.05, 0.1, 0.2),
+  catchment_area_m2 = 1000,
+  swmm_exe = paths$swmm_exe
+)
+
+### Green Roof
+gr <- keys.lid::simulate_performances(
+  lid_selected = scenarios[scenarios$lid_name_tidy == "green_roof",],
+  lid_area_fractions = c(0,1),
+  catchment_area_m2 = 1000,
+  swmm_exe = paths$swmm_exe
+)
+
+### Permeable Pavement
+pp <- keys.lid::simulate_performances(
+  lid_selected = scenarios[scenarios$lid_name_tidy == "permeable_pavement",],
+  lid_area_fractions = c(0, 1),
+  catchment_area_m2 = 1000,
+  swmm_exe = paths$swmm_exe
+)
+
+### Rain Barrel
+rb <- keys.lid::simulate_performances(
+  lid_selected = scenarios[scenarios$lid_name_tidy == "rain_barrel",],
+  lid_area_fractions = c(0, 0.1, 0.2, 0.4),
+  catchment_area_m2 = 1000,
+  swmm_exe = paths$swmm_exe
+)
+
+
+performances <- br %>%
+  dplyr::bind_rows(gr) %>%
+  dplyr::bind_rows(pp) %>%
+  dplyr::bind_rows(rb)
+
+#saveRDS(object = performances, file = "performances.rds")
+#readRDS(file = "performances.rds")
+
+### Check different compression formats (as recommended by Rcmdcheck):
+### https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Data-in-packages
+
+#usethis::use_data(performances, compress = "gzip", overwrite = TRUE) #44.6MB
+#usethis::use_data(performances, compress = "bzip2", overwrite = TRUE) #18.9MB
+usethis::use_data(performances, compress = "xz", overwrite = TRUE) #0.6MB
diff --git a/data/performances.rda b/data/performances.rda
new file mode 100644
index 0000000..8cb75b2
Binary files /dev/null and b/data/performances.rda differ
diff --git a/inst/extdata/scenarios/models/.gitignore b/inst/extdata/scenarios/models/.gitignore
new file mode 100644
index 0000000..abb553b
--- /dev/null
+++ b/inst/extdata/scenarios/models/.gitignore
@@ -0,0 +1,3 @@
+tmp.ini
+tmp.out
+tmp.rpt
diff --git a/inst/extdata/scenarios/models/model_template.inp b/inst/extdata/scenarios/models/model_template.inp
new file mode 100644
index 0000000..fc12562
--- /dev/null
+++ b/inst/extdata/scenarios/models/model_template.inp
@@ -0,0 +1,142 @@
+[TITLE]
+;;Project Title/Notes
+
+[OPTIONS]
+;;Option             Value
+FLOW_UNITS           LPS
+INFILTRATION         HORTON
+FLOW_ROUTING         KINWAVE
+LINK_OFFSETS         DEPTH
+MIN_SLOPE            0
+ALLOW_PONDING        NO
+SKIP_STEADY_STATE    NO
+
+IGNORE_SNOWMELT      YES
+START_DATE           04/30/2008
+START_TIME           00:00:00
+REPORT_START_DATE    04/30/2008
+REPORT_START_TIME    00:00:00
+END_DATE             10/15/2019
+END_TIME             23:00:00
+SWEEP_START          01/01
+SWEEP_END            12/31
+DRY_DAYS             0
+REPORT_STEP          01:00:00
+WET_STEP             01:00:00
+DRY_STEP             01:00:00
+ROUTING_STEP         01:00:00
+RULE_STEP            00:00:00
+
+INERTIAL_DAMPING     PARTIAL
+NORMAL_FLOW_LIMITED  BOTH
+FORCE_MAIN_EQUATION  H-W
+VARIABLE_STEP        0.75
+LENGTHENING_STEP     0
+MIN_SURFAREA         1.14
+MAX_TRIALS           8
+HEAD_TOLERANCE       0.0015
+SYS_FLOW_TOL         5
+LAT_FLOW_TOL         5
+MINIMUM_STEP         0.5
+THREADS              1
+
+[EVAPORATION]
+;;Data Source    Parameters
+;;-------------- ----------------
+TEMPERATURE
+DRY_ONLY         NO
+
+[TEMPERATURE]
+;;Data Element     Values
+FILE               "swmm_climeng_zone1_temp.txt"
+WINDSPEED FILE
+SNOWMELT           0 0.5 0.6 18 39 0
+ADC IMPERVIOUS     1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
+ADC PERVIOUS       1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
+
+[RAINGAGES]
+;;Name           Format    Interval SCF      Source
+;;-------------- --------- ------ ------ ----------
+RainGage         VOLUME    1:00     1        FILE       "swmm_bwsti_zone1_rain_hourly.txt" BWSTI      MM
+
+[SUBCATCHMENTS]
+;;Name           Rain Gage        Outlet           Area     %Imperv  Width    %Slope   CurbLen  SnowPack
+;;-------------- ---------------- ---------------- -------- -------- -------- -------- -------- ----------------
+S1               RainGage         Out1             0.0065   100      6        0.5      0        snowPack1
+
+[SUBAREAS]
+;;Subcatchment   N-Imperv   N-Perv     S-Imperv   S-Perv     PctZero    RouteTo    PctRouted
+;;-------------- ---------- ---------- ---------- ---------- ---------- ---------- ----------
+S1               0.01       0.1        1          25         0          OUTLET
+
+[INFILTRATION]
+;;Subcatchment   MaxRate    MinRate    Decay      DryTime    MaxInfil
+;;-------------- ---------- ---------- ---------- ---------- ----------
+S1               700        350        80         5          90
+
+[LID_CONTROLS]
+;;Name           Type/Layer Parameters
+;;-------------- ---------- ----------
+Gr2              GR
+Gr2              SURFACE    0          0.313411733086687 0.0845782298611011 6.94508363259956 5
+Gr2              SOIL       108.799609355628 0.599323754012585 0.378954279981554 0.211478835605085 170.699292724021 58.6889489809982 78.9419485256076
+Gr2              DRAINMAT   65.0779889663681 0.252694211550988 0.400062839913182
+
+[LID_USAGE]
+;;Subcatchment   LID Process      Number  Area       Width      InitSat    FromImp    ToPerv     RptFile                  DrainTo          FromPerv
+;;-------------- ---------------- ------- ---------- ---------- ---------- ---------- ---------- ------------------------ ---------------- ----------
+S1               Gr2              1       65         6          0          0          0          *                        *                0
+
+[SNOWPACKS]
+;;Name           Surface    Parameters
+;;-------------- ---------- ----------
+snowPack1        PLOWABLE   0.0001     0.0002     4          0.5        0.00       0.00       0.0
+snowPack1        IMPERVIOUS 0.0001     0.0002     4          0.5        0.00       0.00       50
+snowPack1        PERVIOUS   0.0001     0.0002     4          0.5        0.00       0.00       50
+snowPack1        REMOVAL    1.0        0.0        0.0        0.0        0.0        0.0
+
+[OUTFALLS]
+;;Name           Elevation  Type       Stage Data       Gated    Route To
+;;-------------- ---------- ---------- ---------------- -------- ----------------
+Out1             0          FREE                        NO
+
+[PATTERNS]
+;;Name           Type       Multipliers
+;;-------------- ---------- -----------
+Recovery         MONTHLY    1.5   1.5   1.5   1.5   1.5   1.5
+Recovery                    1.5   1.5   1.5   1.5   1.5   1.5
+
+[REPORT]
+;;Reporting Options
+SUBCATCHMENTS ALL
+NODES ALL
+LINKS ALL
+
+[TAGS]
+
+[MAP]
+DIMENSIONS 0.000 0.000 10000.000 10000.000
+Units      None
+
+[COORDINATES]
+;;Node           X-Coord            Y-Coord
+;;-------------- ------------------ ------------------
+Out1             3384.528           6666.667
+
+[VERTICES]
+;;Link           X-Coord            Y-Coord
+;;-------------- ------------------ ------------------
+
+[Polygons]
+;;Subcatchment   X-Coord            Y-Coord
+;;-------------- ------------------ ------------------
+S1               4613.197           6416.382
+S1               4670.080           4232.082
+S1               1985.210           4288.965
+S1               1962.457           6439.135
+
+[SYMBOLS]
+;;Gage           X-Coord            Y-Coord
+;;-------------- ------------------ ------------------
+RainGage         1598.407           5426.621
+
diff --git a/inst/extdata/scenarios/swmm_lid-parameterisation.xlsx b/inst/extdata/scenarios/swmm_lid-parameterisation.xlsx
index 48a2a65..34745ae 100644
Binary files a/inst/extdata/scenarios/swmm_lid-parameterisation.xlsx and b/inst/extdata/scenarios/swmm_lid-parameterisation.xlsx differ
diff --git a/man/boxplot_runoff_max.Rd b/man/boxplot_runoff_max.Rd
new file mode 100644
index 0000000..521afa3
--- /dev/null
+++ b/man/boxplot_runoff_max.Rd
@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/plot.R
+\name{boxplot_runoff_max}
+\alias{boxplot_runoff_max}
+\title{Boxplot Runoff Maximum per Event}
+\usage{
+boxplot_runoff_max(
+  lid = "bioretention_cell",
+  zone_id = 1,
+  performances = keys.lid::performances
+)
+}
+\arguments{
+\item{lid}{tidy name of LID (default: "bioretention_cell")}
+
+\item{zone_id}{climate zone id to plot (default: 1)}
+
+\item{performances}{nested tibble (default: \code{\link{performances}})}
+}
+\value{
+interactive plot of performance results
+}
+\description{
+Boxplot Runoff Maximum per Event
+}
+\examples{
+\dontrun{
+boxplot_runoff_max(lid = "bioretention_cell", zone_id = 1)
+}
+}
diff --git a/man/boxplot_runoff_volume.Rd b/man/boxplot_runoff_volume.Rd
new file mode 100644
index 0000000..f334742
--- /dev/null
+++ b/man/boxplot_runoff_volume.Rd
@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/plot.R
+\name{boxplot_runoff_volume}
+\alias{boxplot_runoff_volume}
+\title{Boxplot Runoff Volume per Event}
+\usage{
+boxplot_runoff_volume(
+  lid = "bioretention_cell",
+  zone_id = 1,
+  performances = keys.lid::performances
+)
+}
+\arguments{
+\item{lid}{tidy name of LID (default: "bioretention_cell")}
+
+\item{zone_id}{climate zone id to plot (default: 1)}
+
+\item{performances}{nested tibble (default: \code{\link{performances}})}
+}
+\value{
+interactive plot of performance results
+}
+\description{
+Boxplot Runoff Volume per Event
+}
+\examples{
+\dontrun{
+boxplot_runoff_volume(lid = "bioretention_cell", zone_id = 1)
+}
+}
diff --git a/man/boxplot_vrr.Rd b/man/boxplot_vrr.Rd
new file mode 100644
index 0000000..1a7b035
--- /dev/null
+++ b/man/boxplot_vrr.Rd
@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/plot.R
+\name{boxplot_vrr}
+\alias{boxplot_vrr}
+\title{Boxplot Volume Rainfall Retended per Year}
+\usage{
+boxplot_vrr(
+  lid = "bioretention_cell",
+  zone_id = 1,
+  performances = keys.lid::performances
+)
+}
+\arguments{
+\item{lid}{tidy name of LID (default: "bioretention_cell")}
+
+\item{zone_id}{climate zone id to plot (default: 1)}
+
+\item{performances}{nested tibble (default: \code{\link{performances}})}
+}
+\value{
+interactive plot of performance results
+}
+\description{
+Boxplot Volume Rainfall Retended per Year
+}
+\examples{
+\dontrun{
+boxplot_vrr(lid = "bioretention_cell", zone_id = 1)
+}
+}
diff --git a/man/lidconfig_to_swmm.Rd b/man/lidconfig_to_swmm.Rd
new file mode 100644
index 0000000..d00285e
--- /dev/null
+++ b/man/lidconfig_to_swmm.Rd
@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/lidconfig_to_swmm.R
+\name{lidconfig_to_swmm}
+\alias{lidconfig_to_swmm}
+\title{Convert LID config to SWMM LID controls}
+\usage{
+lidconfig_to_swmm(df)
+}
+\arguments{
+\item{df}{data frame for a single scenario of a LID (as returned by
+\code{\link{read_scenarios}})}
+}
+\value{
+data frame with SWMM LID controls
+}
+\description{
+Convert LID config to SWMM LID controls
+}
+\examples{
+scenarios <- keys.lid::read_scenarios()
+unique(scenarios$lid_name_tidy)
+lid <- "permeable_pavement"
+lid_selected <- scenarios \%>\%  dplyr::filter(.data$lid_name_tidy == lid)
+scenario_names <- unique(lid_selected$scenario_name)
+scenario_name <- scenario_names[1]
+scenario_name
+lid_selected_scenario <- lid_selected[lid_selected$scenario_name == scenario_name,]
+lid_controls <- lidconfig_to_swmm(lid_selected_scenario)
+str(lid_controls)
+}
diff --git a/man/performances.Rd b/man/performances.Rd
new file mode 100644
index 0000000..67ba23a
--- /dev/null
+++ b/man/performances.Rd
@@ -0,0 +1,35 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/performances.R
+\docType{data}
+\name{performances}
+\alias{performances}
+\title{Performance results for LIDs}
+\format{
+A nested tibble with 290 rows and 16 variables:
+\describe{
+\item{zone_id}{climate zone id}
+\item{lid_name_tidy}{tidy LID name}
+\item{scenario_name}{name of LID scenario}
+\item{catchment_area_m2}{catchment area in squaremeters}
+\item{lid_area_fraction}{fraction of LID compared to total catchment}
+\item{lid_area_m2}{total LID area}
+\item{lid_usage}{tibble with LID usage parameterisation}
+\item{lid_controls}{tibble with LID controls parameterisation}
+\item{subcatchment}{tibble with subcatchment parameterisation}
+\item{annual}{tibble with two columns "year" and "vrr" (volume rainfall retended for each year}
+\item{events_max}{tibble with maximum values for each rainfall event}
+\item{events_sum}{tibble with sum values for each rainfall event}
+\item{col_eventsep}{name of SWMM results used for event separation}
+\item{model_inp}{path to SWMM model input file}
+\item{model_rpt}{path to SWMM model report file}
+\item{model_out}{path to SWMM model output file}
+}
+}
+\usage{
+performances
+}
+\description{
+A dataset containing the performance of LIDs for different climate conditions
+created with R script in /data-raw/performances.R
+}
+\keyword{datasets}
diff --git a/man/pipe.Rd b/man/pipe.Rd
new file mode 100644
index 0000000..a648c29
--- /dev/null
+++ b/man/pipe.Rd
@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-pipe.R
+\name{\%>\%}
+\alias{\%>\%}
+\title{Pipe operator}
+\usage{
+lhs \%>\% rhs
+}
+\arguments{
+\item{lhs}{A value or the magrittr placeholder.}
+
+\item{rhs}{A function call using the magrittr semantics.}
+}
+\value{
+The result of calling \code{rhs(lhs)}.
+}
+\description{
+See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
+}
+\keyword{internal}
diff --git a/man/plot_vrr_median.Rd b/man/plot_vrr_median.Rd
new file mode 100644
index 0000000..b0184ed
--- /dev/null
+++ b/man/plot_vrr_median.Rd
@@ -0,0 +1,28 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/plot.R
+\name{plot_vrr_median}
+\alias{plot_vrr_median}
+\title{Plot Median VRR}
+\usage{
+plot_vrr_median(
+  lid = "bioretention_cell",
+  performances = keys.lid::performances
+)
+}
+\arguments{
+\item{lid}{tidy name of LID}
+
+\item{performances}{nested tibble (default: \code{\link{performances}})}
+}
+\value{
+interactive plot of performance results
+}
+\description{
+Plot Median VRR
+}
+\examples{
+\dontrun{
+lids <- unique(keys.lid::performances$lid_name_tidy)
+sapply(lids, function(lid) print(keys.lid::plot_vrr_median(lid)))
+}
+}
diff --git a/man/simulate_performance.Rd b/man/simulate_performance.Rd
new file mode 100644
index 0000000..3711729
--- /dev/null
+++ b/man/simulate_performance.Rd
@@ -0,0 +1,62 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/simulate_performance.R
+\name{simulate_performance}
+\alias{simulate_performance}
+\title{Simulate Performance of LID}
+\usage{
+simulate_performance(
+  lid_selected,
+  lid_area_fraction = 0,
+  catchment_area_m2 = 1000,
+  col_eventsep = "total_rainfall",
+  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{
+\item{lid_selected}{tibble with a selected LID as retrieved by \code{\link{read_scenarios}}}
+
+\item{lid_area_fraction}{fraction of LID in subcatchment (default: 0)}
+
+\item{catchment_area_m2}{catchment area (default: 1000 m2)}
+
+\item{col_eventsep}{SWMM output column used for event separation (default:
+"total_rainfall")}
+
+\item{swmm_base_inp}{path to SWMM model to be used as template for modification
+(default: keys.lid::extdata_file("scenarios/models/model_template.inp"))}
+
+\item{swmm_climate_dir}{directory with climate data
+(default: keys.lid::extdata_file("rawdata/weather_sponge_regions")}
+
+\item{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)}
+
+\item{model_dir}{default:  keys.lid::extdata_file("scenarios/models")}
+
+\item{zone_ids}{climate zone ids to be used for simulation (default: 1L:5L)}
+}
+\value{
+tibble with nested lists containing all scenario performance
+}
+\description{
+Simulate Performance of LID
+}
+\examples{
+\dontrun{
+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)
+}
+}
diff --git a/man/simulate_performances.Rd b/man/simulate_performances.Rd
new file mode 100644
index 0000000..d84ee66
--- /dev/null
+++ b/man/simulate_performances.Rd
@@ -0,0 +1,61 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/simulate_performances.R
+\name{simulate_performances}
+\alias{simulate_performances}
+\title{Simulate Performances of LID}
+\usage{
+simulate_performances(
+  lid_selected,
+  lid_area_fractions = c(0, 1),
+  catchment_area_m2 = 1000,
+  col_eventsep = "total_rainfall",
+  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{
+\item{lid_selected}{tibble with a selected LID as retrieved by \code{\link{read_scenarios}}}
+
+\item{lid_area_fractions}{fractions of LID in subcatchment (default: c(0,1)}
+
+\item{catchment_area_m2}{catchment area (default: 1000 m2)}
+
+\item{col_eventsep}{SWMM output column used for event separation (default:
+"total_rainfall")}
+
+\item{swmm_base_inp}{path to SWMM model to be used as template for modification
+(default: keys.lid::extdata_file("scenarios/models/model_template.inp"))}
+
+\item{swmm_climate_dir}{directory with climate data
+(default: keys.lid::extdata_file("rawdata/weather_sponge_regions")}
+
+\item{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)}
+
+\item{model_dir}{default:  keys.lid::extdata_file("scenarios/models")}
+
+\item{zone_ids}{climate zone ids to be used for simulation (default: 1L:5L)}
+}
+\value{
+tibble with nested lists containing all scenario performances for
+varying lid_area_fractions
+}
+\description{
+Simulate Performances of LID
+}
+\examples{
+\dontrun{
+scenarios <- keys.lid::read_scenarios()
+unique(scenarios$lid_name_tidy)
+lid <- "permeable_pavement"
+lid_selected <- scenarios \%>\%  dplyr::filter(.data$lid_name_tidy == lid)
+pp <- keys.lid::simulate_performances(lid_selected,
+                                      lid_area_fractions = c(0,1)
+                                      )
+}
+}
diff --git a/vignettes/scenarios.Rmd b/vignettes/scenarios.Rmd
index 33e3657..0529cc4 100644
--- a/vignettes/scenarios.Rmd
+++ b/vignettes/scenarios.Rmd
@@ -12,12 +12,189 @@ knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
+
+#keys.lid::performances
 ```
 
+## Scenarios
+
 ```{r setup}
 library(keys.lid)
-lid_scenarios <- keys.lid::read_scenarios()
 
-DT::datatable(lid_scenarios)
+paths_list <- list(
+  swmm_exe = "C:/Program Files (x86)/EPA SWMM 5.1.015/swmm5.exe"
+  )
+
+paths <- kwb.utils::resolve(paths_list)
+
+
+scenarios <- keys.lid::read_scenarios()
+
+DT::datatable(scenarios)
+
+
+```
+## Simulate Performance
+
+The SWMM simulations (years: 2008 - 2019) and calculation of 
+performance metrics (volume rainfall retended, rainfall event sums/max) for four 
+LIDs with different parameterisations and for five different climate zones 
+in China (in total: 290 model runs) takes about 2h 20min (~ 30 seconds per model 
+run) on my laptop.
+
+```{r simulate_performances, eval = FALSE}
+
+### Bioretention Cell
+br <- keys.lid::simulate_performances(
+  lid_selected = scenarios[scenarios$lid_name_tidy == "bioretention_cell",],
+  lid_area_fractions = c(0, 0.05, 0.1, 0.2),
+  catchment_area_m2 = 1000,
+  swmm_exe = paths$swmm_exe
+)
+
+### Green Roof
+gr <- keys.lid::simulate_performances(
+  lid_selected = scenarios[scenarios$lid_name_tidy == "green_roof",],
+  lid_area_fractions = c(0,1),
+  catchment_area_m2 = 1000,
+  swmm_exe = paths$swmm_exe
+)
+
+### Permeable Pavement
+pp <- keys.lid::simulate_performances(
+  lid_selected = scenarios[scenarios$lid_name_tidy == "permeable_pavement",],
+  lid_area_fractions = c(0, 1),
+  catchment_area_m2 = 1000,
+  swmm_exe = paths$swmm_exe
+)
+
+### Rain Barrel
+rb <- keys.lid::simulate_performances(
+  lid_selected = scenarios[scenarios$lid_name_tidy == "rain_barrel",],
+  lid_area_fractions = c(0, 0.1, 0.2, 0.4),
+  catchment_area_m2 = 1000,
+  swmm_exe = paths$swmm_exe
+)
+
+
+performances <- br %>%
+  dplyr::bind_rows(gr) %>%
+  dplyr::bind_rows(pp) %>%
+  dplyr::bind_rows(rb)
+```
+
+## Evaluate Performance
+
+### Median Volume Rainfall Retended per Year
+
+#### Bioretention Cell 
+
+```{r evaluate_performance_vrr_br, fig.height=7, fig.width=7}
+
+keys.lid::plot_vrr_median("bioretention_cell")
+
+```
+
+#### Green Roof 
+
+```{r evaluate_performance_vrr_gr, fig.height=7, fig.width=7}
+
+keys.lid::plot_vrr_median("green_roof")
+
+```
+
+#### Permeable Pavements 
+
+```{r evaluate_performance_vrr_pp, fig.height=7, fig.width=7}
+
+keys.lid::plot_vrr_median("permeable_pavement")
+
+```
+
+#### Rain Barrel
+
+```{r evaluate_performance_vrr_rr, fig.height=7, fig.width=7}
+
+keys.lid::plot_vrr_median("rain_barrel")
+
+```
+
+### Boxplots
+
+Example for LID **green roof** in **climate zones 1 and 5**
+
+#### Volume Rainfall Retended 
+
+**Zone 1**
+
+```{r evaluate_performance_boxplot_vrr_gr_zone1, fig.height=7, fig.width=7}
+
+keys.lid::boxplot_vrr(lid = "green_roof", zone_id = 1)
+
+```
+
+**Zone 2**
+
+```{r evaluate_performance_boxplot_vrr_gr_zone2, fig.height=7, fig.width=7}
+
+keys.lid::boxplot_vrr(lid = "green_roof", zone_id = 2)
+
+```
+
+**Zone 3**
+
+```{r evaluate_performance_boxplot_vrr_gr_zone3, fig.height=7, fig.width=7}
+
+keys.lid::boxplot_vrr(lid = "green_roof", zone_id = 3)
+
+```
+
+**Zone 4**
+
+```{r evaluate_performance_boxplot_vrr_gr_zone4, fig.height=7, fig.width=7}
+
+keys.lid::boxplot_vrr(lid = "green_roof", zone_id = 4)
+
+```
+
+**Zone 5**
+
+```{r evaluate_performance_boxplot_vrr_gr_zone5, fig.height=7, fig.width=7}
+
+keys.lid::boxplot_vrr(lid = "green_roof", zone_id = 5)
+
+```
+
+#### Runoff Maximum per Event 
+
+**Zone 1**
+
+```{r evaluate_performance_boxplot_runoff_max_gr_zone1, fig.height=7, fig.width=7}
+
+keys.lid::boxplot_runoff_max(lid = "green_roof", zone_id = 1)
+
+```
+**Zone 5**
+
+```{r evaluate_performance_boxplot_runoff_max_gr_zone5, fig.height=7, fig.width=7}
+
+keys.lid::boxplot_runoff_max(lid = "green_roof", zone_id = 5)
+
+```
+
+#### Runoff Volume per Event 
+
+**Zone 1**
+
+```{r evaluate_performance_boxplot_runoff_volume_gr_zone1, fig.height=7, fig.width=7}
+
+keys.lid::boxplot_runoff_volume(lid = "green_roof", zone_id = 1)
+
+```
+**Zone 5**
+
+```{r evaluate_performance_boxplot_runoff_volume_gr_zone5, fig.height=7, fig.width=7}
+
+keys.lid::boxplot_runoff_volume(lid = "green_roof", zone_id = 5)
 
 ```
diff --git a/vignettes/sensitivity.Rmd b/vignettes/sensitivity.Rmd
index 1b02c44..ece1707 100644
--- a/vignettes/sensitivity.Rmd
+++ b/vignettes/sensitivity.Rmd
@@ -188,7 +188,7 @@ for(i in seq_len(l)){
                                 Qcolumn = 'runoff')
     rainfall_volume <- sum(yearj$rainfall_depth, na.rm = TRUE)
 
-    vrr[j] <- runoff_volume/rainfall_volume
+    vrr[j] <- 1 - runoff_volume/rainfall_volume
   }
 
   sensitivity_results[i,