### 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$abimo_exe)
# postprocessing ----------------------------------------------------------------
# post-process ABIMO output file -> join it with input shape file for
# visualization in GIS
scenario_results_jinxi <- urbanAnnualRunoff::get_scenario_results(paths)
scenario_results_jinxi %>%
dplyr::select(!tidyselect::all_of(c("abimo_inpout", "abimo_inpout_emissions"))) %>%
openxlsx::write.xlsx(file = "scenario_results_jinxi.xlsx",
overwrite = TRUE)
#usethis::use_data(scenario_results_jinxi, overwrite = TRUE)Scenario Results
Below is a summary table with the ABIMO water balance modelling results for the different de-coupling scenarios. Combining (combi_xxx) the different single measures, i.e. bioretention cells (for streets), green roofs (for sealed areas with buildings), and permeable pavements (for sealed areas without buildings/streets)) enables to satisfy the VRR (volume rainfall retended) goal defined for climate zone 4, i.e 0.70 >= VRR <= 0.85.
library(urbanAnnualRunoff)
DT::datatable(urbanAnnualRunoff::scenario_results_jinxi %>% dplyr::select(!tidyselect::all_of(c("abimo_inpout", "abimo_inpout_emissions"))))Land Use Classification Comparison
landuse_hit <- shapefiles::read.dbf(paths$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
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")