---
title: "Workflow: Jinxi"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Workflow: Jinxi}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r workflow, eval = FALSE}
### prerequisites (64bit R)
if(Sys.getenv("R_ARCH") != "/x64") {
  stop("Spatial operations need a lot of RAM. Use of 64bit R is required. 
  
Workflow in RStudio:  
1. Go to 'Tools' (top pane)
2. Select 'Global Options' 
3. Select 'General' 
4. Under 'R Sessions' select 'Change' 
5. Select 'Use the machine`s default R version of R64 (64-bit)'
6. Close all R Studio sessions  
7. Restart R Studio"
)
} else {
  
path_list <- list(
  root_path = "C:/kwb/projects/keys/Data-Work packages",
  site = "Jinxi",
  data = "WP2_SUW_pollution_<site>",
  data_hit = "<root_path>/<data>/_DataHIT",
  abimo = "<root_path>/<data>/_DataAnalysis/abimo",
  abimo_inp_shp = "<abimo>/abimo_<site>.shp",
  abimo_inp_dbf = "<abimo>/abimo_<site>.dbf",
  abimo_out = "abimo_<site>out.dbf",
  abimo_berlin = "<abimo>/abimo_2019_mitstrassen.dbf",
  abimo_scenarios = "<abimo>/scenarios/de-coupling",
  abimo_config_original = "<abimo>/config_original.xml",
  abimo_config_modified = "<abimo>/config.xml",
  abimo_exe = "<abimo>/Abimo3_2.exe",
  gis = "<root_path>/<data>/_DataAnalysis/gis",
  climate = "<root_path>/<data>/_DataAnalysis/climate",
  emissions_input = "<root_path>/<data>/_DataAnalysis/emissions/input/annual_mean_conc.csv",
  emissions_output = "<root_path>/<data>/_DataAnalysis/emissions/output",
  landuse_hit = "<gis>/manual_processing/landuse_hit.dbf",
  landuse_kwb = "<gis>/manual_processing/landuse_kwb.tif",
  catchments_hit_all =  "<gis>/manual_processing/catchments_hit_all_area.dbf",
  catchments_hit_abimo =  "<gis>/manual_processing/catchments_hit_abimo_area.dbf",
  landuse_authority = "<data_hit>/2021-03-31_HIT_landuse-per-subcatchment/subcatchments_landuse_v1.0.0.xlsx",
  swmm_annualrunoff = "<data_hit>/WP2_Data_HIT_20201023/swmm_annual_runoff_v1.0.0.txt",
  sewer_connection = "<data_hit>/2021-10-18_email/cleaned/subcatchments_sewer-connection.xls"
)

paths <- kwb.utils::resolve(path_list)


# load shapefile containing subcatchments ('blockteilflächen')
abimo_org <- raster::shapefile(file.path(paths$gis, 'input_subareas.shp'))

# make ABIMO 'CODE' column
names(abimo_org@data) <- "CODE" 

# pad CODE with zeroes to match output of ABIMO
abimo_org@data$CODE <- urbanAnnualRunoff::padCODE(abimo_org@data$CODE)

# build classification model
kwb.ml::buildClassMod(
  rawdir = paths$gis,
  image = 'input_image.img',
  groundTruth = 'input_groundtruth.shp',
  groundTruthValues = list('roof' = 1, 
                           'street' = 2,
                           'pervious' = 3,
                           'shadow' = 4,
                           'water' = 5),
  spectrSigName = 'spectrSig.Rdata',
  modelName = 'rForest.Rdata',
  overlayExists = FALSE,
  nCores = parallel::detectCores() - 1,
  mtryGrd = 1:2, 
  ntreeGrd=seq(80, 150, by=10),
  nfolds = 3, 
  nodesize = 3, 
  cvrepeats = 2)

# check model performance
load(file.path(paths$gis,"rForest.Rdata"))
caret::confusionMatrix(data = model$finalModel$predicted, 
                       reference = model$trainingData$.outcome, 
                       mode = 'prec_recall')
                       
# classify image for roofs and streets
kwb.ml::predictSurfClass(rawdir = paths$gis,
                                    modelName = 'rForest.Rdata',
                                    image = 'input_image.img',
                                    predName = 'classified_image.img')

# make overlay object (list where each element is a vector of the pixel values 
# of the classified image for each subcatchment)
urbanAnnualRunoff::makeOverlay(rawdir = paths$gis,
                               rasterData = 'classified_image.img',
                               subcatchmSPobject = abimo_org,
                               overlayName = 'surfType')

# compute ABIMO variable STR_FLGES (m2 street area)
abimo_org@data$STR_FLGES <- urbanAnnualRunoff::makeSTR_FLGES(
  rawdir = paths$gis,
  subcatchmSPobject = abimo_org,
  mask = 'input_maskUrbanCore.shp',
  rasterData = 'classified_image.img',
  overlayName = 'surfType',
  targetValue = 2, 
  add_streets_outside_subcatchments = FALSE
  )

# make ABIMO variable FLGES
abimo_org@data$FLGES <- urbanAnnualRunoff::makeFLGES(subcatchmSPobject = abimo_org) - abimo_org@data$STR_FLGES

# compute ABIMO variable VG (% soil sealing)
abimo_org@data$VG <- urbanAnnualRunoff::makeVG(rawdir = paths$gis,
                        subcatchmSPobject = abimo_org,
                        rasterData = 'input_impervious.tif',
                        targetValue = 80)


# compute ABIMO variable PROBAU (%roof)
abimo_org@data$PROBAU <- urbanAnnualRunoff::makePROBAU(
  rawdir = paths$gis,
  rasterData = 'classified_image.img',
  overlayName = 'surfType',
  targetValue = 1)

### fix previously wrong calculated area shares (PROBAU, PROVGU, STR_FLGES)
abimo <- urbanAnnualRunoff::fix_abimo_shares(abimo_org)

## Decoupling from SWMM modelling: 
## https://kwb-r.github.io/keys.lid/articles/scenarios.html#green-roof
##
# 100 % green-roof: with_berm_intensive_no-drainmat
#abimo@data$PROBAU <- (1-1*(90.42-15.2)/100)*abimo@data$PROBAU # combi-1_high
# 50 % green-roof: with_berm_intensive_no-drainmat
#abimo@data$PROBAU <- (1-0.5*(90.42-15.2)/100)*abimo@data$PROBAU # combi-1_medium
# 25 % green-roof: with_berm_intensive_no-drainmat
#abimo@data$PROBAU <- (1-0.25*(90.42-15.2)/100)*abimo@data$PROBAU # combi-1_low
# 100 % green-roof: with_berm_extensive_no-drainmat
#abimo@data$PROBAU <- (1-1*(72.6-15.2)/100)*abimo@data$PROBAU # combi-2_high
# 50 % green-roof: with_berm_extensive_no-drainmat
#abimo@data$PROBAU <- (1-0.5*(72.6-15.2)/100)*abimo@data$PROBAU # combi-2_medium
# 25 % green-roof: with_berm_extensive_no-drainmat
#abimo@data$PROBAU <- (1-0.25*(72.6-15.2)/100)*abimo@data$PROBAU # combi-2_low


## Decoupling from SWMM modelling: 
## https://kwb-r.github.io/keys.lid/articles/scenarios.html#permeable-pavements
## provgu <- abimo@data$PROVGU
## abimo@data$PROVGU <- provgu
# 100 % permeable pavements: 180mm.per.hour_no-drainage
#abimo@data$PROVGU <- (1-100*(0.572-0.152)/50)*abimo@data$PROVGU # combi_high
#  50 % permeable pavements: 180mm.per.hour_no-drainage
#abimo@data$PROVGU <- (1-50*(0.572-0.152)/50)*abimo@data$PROVGU # combi_medium
#  25 % permeable pavements: 180mm.per.hour_no-drainage
#abimo@data$PROVGU <- (1-25*(0.572-0.152)/50)*abimo@data$PROVGU # combi_low
#  10 % permeable pavements: 180mm.per.hour_no-drainage
#abimo@data$PROVGU <- (1-10*(0.572-0.152)/50)*abimo@data$PROVGU 

# use raw code to compute and assign remaining ABIMO variables manually

### use Berlin Abimo data (for 'Blockteilflaechen' connected to channelisation ### to imrpove default parameterisation for 'unkown' underground types in Beijing 
abimo_berlin <- foreign::read.dbf(paths$abimo_berlin) %>% 
  dplyr::filter(KANAL == 1)

share_vg_str <- colSums(abimo_berlin[, stringr::str_detect(names(abimo_berlin), "^VGSTRASSE"), drop = FALSE] * abimo_berlin$STR_FLGES /sum(abimo_berlin$STR_FLGES))
                       
share_belag <- colSums(abimo_berlin[, stringr::str_detect(names(abimo_berlin), "^BELAG")] * abimo_berlin$FLGES /sum(abimo_berlin$FLGES))
share_belag <- share_belag*100/sum(share_belag)

share_belag_str <- colSums(abimo_berlin[, stringr::str_detect(names(abimo_berlin), "^STR_BELAG")] * abimo_berlin$STR_FLGES /sum(abimo_berlin$STR_FLGES))
share_belag_str <- share_belag_str*100/sum(share_belag_str)

share_kan <- colSums(abimo_berlin[, stringr::str_detect(names(abimo_berlin), "^KAN_")] * (abimo_berlin$FLGES+abimo_berlin$STR_FLGES) /sum(abimo_berlin$FLGES+abimo_berlin$STR_FLGES))

# % imperviousness streets
abimo@data$VGSTRASSE <- share_vg_str #100 -> 90.0402

## Decoupling from SWMM modelling: 
## https://kwb-r.github.io/keys.lid/articles/scenarios.html#bioretention-cell
## scenario: 3.6mm.per.h_mulde_no-drainage

# 40 % of street area (unsealed area used as bioretention-cell)
#abimo@data$VGSTRASSE <- round(abimo@data$VGSTRASSE - 40*(57.2-15.2)/50,1) #56.4 # combi_high
# 20 % of street area (unsealed area used as bioretention-cell)
#abimo@data$VGSTRASSE <- round(abimo@data$VGSTRASSE - 20*(57.2-15.2)/50,1) #73.2 # combi_medium
# 10 % of street area (unsealed area used as bioretention-cell)
#abimo@data$VGSTRASSE <- round(abimo@data$VGSTRASSE - 10*(57.2-15.2)/50,1) #81.6 # combi_low
#  5 % of street area (unsealed area used as bioretention-cell)
#abimo@data$VGSTRASSE <- round(abimo@data$VGSTRASSE - 5*(57.2-15.2)/50,1) #85.8

# %cover types in other imperv. areas (PROVGU)
abimo@data$BELAG1 <- share_belag[1] #100 -> 34.69844
abimo@data$BELAG2 <- share_belag[2] #  0 -> 48.03494
abimo@data$BELAG3 <- share_belag[3] #  0 ->  5.54433
abimo@data$BELAG4 <- share_belag[4] #  0 -> 11.72229

# %cover types in street areas
abimo@data$STR_BELAG1 <- share_belag_str[1] #100 -> 51.344865
abimo@data$STR_BELAG2 <- share_belag_str[2] #0 -> 26.501014
abimo@data$STR_BELAG3 <- share_belag_str[3] #0 -> 14.497974
abimo@data$STR_BELAG4 <- share_belag_str[4] #0 ->  7.656147

# identifiers
abimo@data$BEZIRK <- 1
abimo@data$STAGEB <- 1
abimo@data$BLOCK <- 1
abimo@data$TEILBLOCK <- 1
abimo@data$NUTZUNG <- 21
abimo@data$TYP <- 21


# channelization degrees. these are set manually since there is no data
abimo@data$KANAL <- 1
abimo@data$KAN_BEB <- 80
abimo@data$KAN_VGU <- 80
abimo@data$KAN_STR <- 80

# soil field capacity and groundwater level (personal communication, Dr. Lipin Li, 
# Harbin Inst. of Technology)
abimo@data$FELD_30 <- 30
abimo@data$FELD_150 <- 30
abimo@data$FLUR <- 1.17

# compute annual and summer rainfall. 
# *** potential enhancement: ***
# return the multiannual average annual rainfall. care must be taken to exclude
# incomplete years from the average calculation. same for summer rainfall
precipitation <- urbanAnnualRunoff::computeABIMOclimate(
  rawdir = paths$climate,
  file_inp = sprintf('raw_climateeng_precipitation_daily_%s.txt', 
                     paths$site),
  file_out = 'precipitation.txt',
  summer_month_start = 4)
# compute annual and summer ETP. this goes manually into the ABIMO config file
  evapotranspiration <- urbanAnnualRunoff::computeABIMOclimate(
  rawdir = paths$climate,
  file_inp = sprintf('raw_climateeng_etp_daily_%s.txt', 
                     paths$site),
  file_out = 'evapotranspiration.txt',
  summer_month_start = 4)
  
### Merge complete years (i.e. 2014-2019 for Jinxi)
### mean rainfall: 1744mm
climate_data <- dplyr::inner_join(precipitation, evapotranspiration,
                                  by = "year", 
                                  suffix = c(".p", ".etp")) %>%  
  dplyr::mutate(dplyr::across(tidyselect::ends_with("p"), round))

climate_data_mean <- dplyr::bind_cols(tibble::tibble(time_period = "2015-2019"), 
                                      climate_data %>%  dplyr::summarise(
                                        dplyr::across(.cols = tidyselect::starts_with("sum"), 
                                                      .fns = mean)))
openxlsx::write.xlsx(climate_data_mean, "climate_data_mean.xlsx")

#wet year 2320mm (2016)
#climate_data <- climate_data %>%  dplyr::filter(year == "2016")

#dry year 1407mm (2019)
#climate_data <- climate_data %>%  dplyr::filter(year == "2019")

# annual rainfall (this can be automatized by making 'computeABIMOclimate'
# return the multiannual average annual rainfall. care must be taken to exclude
# incomplete years from the average calculation)
round_mean <- function(values) {
  round(mean(values), digits  = 0)
}

### Mean precipitation value for period 2014-2019
abimo@data$REGENJA <- round_mean(climate_data$sum_annual.p)
abimo@data$REGENSO <- round_mean(climate_data$sum_summer.p)

### Mean evapotranspiration value for period 2014-2019
kwb.abimo::abimo_xml_evap(
  file_in = paths$abimo_config_original,
  file_out = paths$abimo_config_modified,
  evap_annual = round_mean(climate_data$sum_annual.etp),
  evap_summer = round_mean(climate_data$sum_summer.etp)
)

### Check modified ABIMO config.xml
kwb.utils::hsOpenWindowsExplorer(paths$abimo_config_modified)

# select required columns and write out new shapefile
requiredCols <- c('CODE', 'BEZIRK', 'STAGEB', 'BLOCK', 
                  'TEILBLOCK', 'NUTZUNG', 'TYP', 'FLGES', 
                  'STR_FLGES', 'PROBAU', 'PROVGU', 'VGSTRASSE',
                  'BELAG1', 'BELAG2', 'BELAG3', 'BELAG4',
                  'STR_BELAG1', 'STR_BELAG2', 'STR_BELAG3', 'STR_BELAG4',
                  'KAN_BEB', 'KAN_VGU', 'KAN_STR',
                  'REGENJA', 'REGENSO',
                  'FELD_30', 'FELD_150', 'FLUR')
abimo@data <- kwb.utils::selectColumns(abimo@data, 
                                       columns = requiredCols)

# format numbers to 0 decimal places (avoid 'pseudo-accuracy')
#abimo@data[, 8:ncol(abimo@data)] <- round(
#  abimo@data[, 8:ncol(abimo@data)],
#  digits = 0)

# change decimal separator to comma
# (ABIMO does not run correctly otherwise) 
abimo@data <- as.data.frame(apply(X=apply(X=abimo@data,
                                         c(1, 2),
                                         FUN=as.character),
                                 c(1, 2),
                                 FUN=gsub,
                                 pattern="\\.",
                                 replacement=","),
                           stringsAsFactors = FALSE)




# write ABIMO input table
raster::shapefile(x=abimo, 
                  filename=paths$abimo_inp_shp,
                  overwrite=TRUE)


kwb.abimo::write.dbf.abimo(abimo, 
                           new_dbf = paths$abimo_inp_dbf)


# run ABIMO manually in GUI ----------------------------------------------------
abimo_out <- sprintf('abimo_%sout.dbf', paths$site)
if(!fs::file_exists(paths$abimo_exe)) {
  stop(cat(sprintf("ABIMO executale does not exist: %s
Please copy to this location", paths$abimo_exe)))
} else {
message(cat(sprintf("Run ABIMO executable manually:
1. Select input file: \"%s\"
2. Save it under: \"%s\"", 
                    abimo_inp_path, 
                    abimo_out))
)
      
kwb.utils::hsOpenWindowsExplorer(paths$abimo_exe)

# postprocessing ----------------------------------------------------------------
# post-process ABIMO output file -> join it with input shape file for 
# visualization in GIS

scenario_results_jinxi <- urbanAnnualRunoff::get_scenario_results(paths)

scenario_results_jinxi %>%  
dplyr::select(!tidyselect::all_of(c("abimo_inpout", "abimo_inpout_emissions"))) %>% 
openxlsx::write.xlsx(file = "scenario_results_jinxi.xlsx",
                     overwrite = TRUE)
#usethis::use_data(scenario_results_jinxi, overwrite = TRUE)


```

## Scenario Results

Below is a summary table with the ABIMO water balance modelling results for the different `de-coupling` scenarios. 
Combining (`combi_xxx`) the different single measures, i.e. `bioretention cells` (for streets), `green roofs` (for sealed areas with buildings),  and `permeable pavements` (for sealed areas without buildings/streets)) enables to satisfy the `VRR` (volume rainfall retended) goal defined for climate zone 4, i.e `0.70 >= VRR <= 0.85`.




```{r scenario_results, eval = TRUE}
library(urbanAnnualRunoff)
DT::datatable(urbanAnnualRunoff::scenario_results_jinxi %>%  dplyr::select(!tidyselect::all_of(c("abimo_inpout", "abimo_inpout_emissions"))))
```


## Land Use Classification Comparison 

```{r landuse_comparison, eval = FALSE}
landuse_hit <- shapefiles::read.dbf(paths$landuse_hit)
landuse_hit_stats <- landuse_hit$dbf %>%  
  dplyr::mutate(landuse_name = kwb.utils::multiSubstitute(.data$landuse, 
                           list(
                           '1' = "pervious",
                           '2' = "pervious",
                           '3' = "roof", 
                           '4' = "street",
                           '5' = "water")
                           )
                ) %>%
  dplyr::group_by(.data$landuse_name) %>% 
  dplyr::summarise(area_m2 = sum(.data$area_m2),
                   area_percent = round(100*area_m2/sum(landuse_hit$dbf$area_m2),1)) %>% 
  dplyr::rename(landuse = .data$landuse_name) %>%  
  dplyr::arrange(.data$landuse) %>% 
  dplyr::select(.data$landuse, .data$area_percent)

landuse_kwb <- raster::raster(paths$landuse_kwb)

landuse_kwb_values <- raster::values(landuse_kwb)
landuse_kwb_values_vector <- table(landuse_kwb_values[!is.na(landuse_kwb_values)])
landuse_kwb_stats <- tibble::tibble(landuse_id = names(landuse_kwb_values_vector),
                                    landuse = kwb.utils::multiSubstitute(landuse_id,
                                                                         list('1' = "roof",
                                                                              '2' = "street",
                                                                              '3' = "pervious",
                                                                              '4' = "shadow",
                                                                              '5' = "water")),
                                    area_m2 = landuse_kwb_values_vector,
                                    area_total = sum(landuse_kwb_values_vector),
                                    area_percent = round(100*area_m2/area_total,1)) %>%  
  dplyr::arrange(.data$landuse) %>% 
  dplyr::select(.data$landuse, .data$area_percent)


catchments_hit_abimo <- shapefiles::read.dbf(paths$catchments_hit_abimo)
names(catchments_hit_abimo$dbf)[5] <- "subcatchment_name"


landuse_authority_data <- readxl::read_xlsx(paths$landuse_authority, 
                                            sheet = "data")
landuse_authority_metadata <- readxl::read_xlsx(paths$landuse_authority, 
                                                sheet = "metadata")

landuse_authority_stats <- landuse_authority_data %>%  
  dplyr::left_join(landuse_authority_metadata,
                   by = "landuse_id") %>% 
  dplyr::right_join(catchments_hit_abimo$dbf[,c("subcatchment_name", "area_m2")],
                   by = "subcatchment_name") %>% 
  dplyr::group_by(.data$landuse_name) %>% 
  dplyr::summarise(percent = round(sum(.data$area_m2 * .data$percent)/sum(catchments_hit_abimo$dbf$area_m2),1)) %>% 
  dplyr::mutate(landuse_name = kwb.utils::multiSubstitute(.data$landuse_name, 
                             list('greenland' = "pervious",
                                  'road' = "streets"
                                  ))) %>% 
  dplyr::arrange(.data$landuse_name)


openxlsx::write.xlsx(list(landuse_hit_stats = landuse_hit_stats,
                          landuse_kwb_stats = landuse_kwb_stats,
                          landuse_authority_stats = landuse_authority_stats ),
                     file = "landuse_classification_comparison.xlsx",
                     overwrite = TRUE
                     )

```

## ABIMO vs SWMM Comparison 

```{r water_balance_comparison, eval = FALSE}

swmm_annualrunoff <- read.table(file = paths$swmm_annualrunoff,  
                                sep = "", 
                                skip = 9, 
                                header = TRUE)


catchments_hit_abimo_connected_100 <- catchments_hit_abimo$dbf[,c("subcatchment_name", "area_m2", "connection")] %>% 
  dplyr::filter(.data$connection == 100)


catchments_hit_abimo_connected_0 <- catchments_hit_abimo$dbf[,c("subcatchment_name", "area_m2", "connection")] %>% 
  dplyr::filter(.data$connection == 0)

swmm_catchment_abimo <- catchments_hit_abimo_connected_100 %>% #catchments_hit_abimo$dbf[,c("subcatchment_name", "area_m2")] 
  dplyr::mutate(area_percent = .data$area_m2/sum(.data$area_m2)) %>% 
  dplyr::left_join(swmm_annualrunoff, by = "subcatchment_name") %>% 
  dplyr::summarise(area_m2 = sum(.data$area_m2),
                   total_precip_mm = sum(.data$area_percent*.data$total_precip_mm),
                   total_evap_mm = sum(.data$area_percent*.data$total_evap_mm),
                   total_infil_mm = sum(.data$area_percent*.data$total_infil_mm),
                   total_runoff_mm = sum(.data$area_percent*.data$total_runoff_mm)) %>% 
  dplyr::mutate(vrr = round((1 - total_runoff_mm/total_precip_mm)*100, 1))

catchments_hit_all <- shapefiles::read.dbf(paths$catchments_hit_all)
names(catchments_hit_all$dbf)[5] <- "subcatchment_name"

swmm_catchment_all <- catchments_hit_all$dbf[,c("subcatchment_name", "area_m2")] %>% 
  dplyr::mutate(area_percent = .data$area_m2/sum(catchments_hit_all$dbf$area_m2)) %>% 
  dplyr::left_join(swmm_annualrunoff, by = "subcatchment_name") %>% 
  dplyr::summarise(total_precip_mm = sum(.data$area_percent*.data$total_precip_mm),
                   total_evap_mm = sum(.data$area_percent*.data$total_evap_mm),
                   total_infil_mm = sum(.data$area_percent*.data$total_infil_mm),
                   total_runoff_mm = sum(.data$area_percent*.data$total_runoff_mm)) %>% 
  dplyr::mutate(vrr = round((1 - total_runoff_mm/total_precip_mm)*100, 1))


swmm_catchment_stats <- dplyr::bind_cols(tibble::tibble(catchment = c("all", "abimo")),
                                         dplyr::bind_rows(swmm_catchment_all, 
                                                      swmm_catchment_abimo)
                                         )

sewer_connection <- readxl::read_xls(paths$sewer_connection)
sewer_connection_stats_abimo <- catchments_hit_abimo$dbf %>% 
  dplyr::mutate(area_percent = .data$area_m2/sum(catchments_hit_abimo$dbf$area_m2)) %>% 
  dplyr::left_join(sewer_connection, by = "subcatchment_name") %>% 
  dplyr::summarise(connection_percent = sum(.data$area_percent*.data$connect))


sewer_connection_stats_all <- catchments_hit_all$dbf %>% 
  dplyr::mutate(area_percent = .data$area_m2/sum(catchments_hit_all$dbf$area_m2)) %>% 
  dplyr::left_join(sewer_connection, by = "subcatchment_name") %>% 
  dplyr::summarise(connection_percent = sum(.data$area_percent*.data$connect))

sewer_connection_stats <- dplyr::bind_cols(tibble::tibble(catchment = c("all", "abimo")),
                                         dplyr::bind_rows(sewer_connection_stats_all, 
                                                      sewer_connection_stats_abimo)
                                         )


urbanAnnualRunoff::read_concentrations(paths$emissions_input)[,c("VariableName", "mean", "UnitsAbbreviation")] %>% 
  openxlsx::write.xlsx("concentrations.xlsx")

```
