file_irrig <- "Y:/WWT_Department/Projects/FlexTreat/Work-packages/AP3/3_1_4_Prognosemodell/Bewässerungsmenge_Braunschweig_2020_2023.xlsx"
irrig <- kwb.db::hsGetTable(file_irrig, tbl = "my_data") %>%
dplyr::select(! tidyselect::contains("mm"))
names(irrig) <- stringr::str_remove(names(irrig), pattern = "\\s.*")
irrig_update <- irrig %>%
dplyr::rename("Monat_num" = "Monat") %>%
dplyr::mutate("Monat" = withr::with_locale(new = c("LC_TIME" = "de_DE"), {
format(as.Date(sprintf("2000-%02d-01", Monat_num)), "%b")
})) %>%
dplyr::relocate(Monat) %>%
dplyr::relocate(Monat_num, .after = Monat) %>%
tidyr::gather(key = "Typ", value = "Menge_m3", - Monat, - Monat_num, - Jahr) %>%
dplyr::mutate(Typ = stringr::str_remove(Typ, "menge")) %>%
dplyr::arrange(Typ, Jahr, Monat_num)
irrig_old <- readr::read_csv2("inst/extdata/input-data/Beregnungsmengen_AVB.csv")
label_to_remove <- sprintf("%d-%02d_%s", irrig_old$Jahr, irrig_old$Monat_num, irrig_old$Typ) %>%
unique()
irrig_update_tmp <- irrig_update %>%
dplyr::mutate(label = sprintf("%d-%02d_%s",Jahr, Monat_num, Typ)) %>%
dplyr::filter(!label %in% label_to_remove) %>%
dplyr::select(-label)
irrig_new <- dplyr::bind_rows(irrig_old, irrig_update_tmp) %>%
dplyr::arrange(Typ, Jahr, Monat_num)
readr::write_csv2(irrig_new, "inst/extdata/input-data/Beregnungsmengen_AVB.csv")
#'## 2700ha (https://www.abwasserverband-bs.de/de/was-wir-machen/verregnung/)
irrigation_area_sqm <- 27000000
irrigation <- irrig_old %>%
dplyr::rename(irrigation_m3 = .data$Menge_m3,
source = .data$Typ,
month = .data$Monat_num,
year = .data$Jahr) %>%
dplyr::mutate(date_start = as.Date(sprintf("%d-%02d-01",
.data$year,
.data$month)),
days_in_month = as.numeric(lubridate::days_in_month(.data$date_start)),
date_end = as.Date(sprintf("%d-%02d-%02d",
.data$year,
.data$month,
.data$days_in_month)),
source = kwb.utils::multiSubstitute(.data$source,
replacements = list("Grundwasser" = "groundwater.mmPerDay",
"Klarwasser" = "clearwater.mmPerDay")),
irrigation_cbmPerDay = .data$irrigation_m3/.data$days_in_month,
irrigation_area_sqm = irrigation_area_sqm,
irrigation_mmPerDay = 1000*irrigation_cbmPerDay/irrigation_area_sqm) %>%
dplyr::select(.data$year,
.data$month,
.data$days_in_month,
.data$date_start,
.data$date_end,
.data$source,
.data$irrigation_mmPerDay,
.data$irrigation_area_sqm) %>%
tidyr::pivot_wider(names_from = .data$source,
values_from = .data$irrigation_mmPerDay)
usethis::use_data(irrigation, overwrite = TRUE)
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