R/Wildlife_Racoon_Sub_Model.R

In kbrannan/bacteriaSourceModels: Models for estimation of bacteria loads from several sources

#'  model bacteria generation from racoons
#'
#'  This function is the bacteria source-model Racoon. The model
#'  generates input for HSPF. The specific outputs from this source model
#'  are loads from the beaver to land and directly to the stream
#'  The load to the land is in the form of load/acre for forest
#'  PLS in a sub-watershed that the source-model contributes to and
#'  the hourly load to the stream in the form of a MUTSIN file. The
#'  input for the model is from an ASCII text file. Use the text below
#'  as a template for the input file. The symbol used for comments in
#'  the input file is "***". The definitions for the symbols used in
#'  the template are: YYYY is four-digit year, MM two-digit month,
#'  DD is the two-digit day, ## is an integer, #.# is a floating point
#'  number, and #.#E+## is a number in scientific notation
#' @param chr.file.input is the input file for the model
#' @export

wildlife.Racoon <- function(chr.file.input) {


## read input files
  df.input <- utils::read.delim(chr.file.input, sep=":",
                          comment.char="*", stringsAsFactors=FALSE,
                          header=FALSE)
  names(df.input) <- c("parameter","value")

  ##
  ## set values for variables

  ## get sub watershed number
  chr.sub <- gsub("([^0-9]){1, }", "" , df.input[df.input$parameter == "Watershed", "value"])

  ## land use information
  lu.pasture.area   <- as.numeric(df.input$value[
    df.input$parameter == "Pasture Area in Watershed (ac)"])
  lu.forest.area   <- as.numeric(df.input$value[
    df.input$parameter == "Forest Area in Watershed (ac)"])
  lu.RAOCUT.area   <- as.numeric(df.input$value[
    df.input$parameter == "Residential/Agricultural Operration Area/Commercial/Urban/Transportation (ac)"])
  ## coyote habitat set to all PLS area
  lu.habitatarea <- lu.pasture.area + lu.forest.area + lu.RAOCUT.area
## animal information
  ## population densities
  amn.density  <- as.numeric(df.input$value[
    df.input$parameter == "Animal Density (animal/ac)"])
  ## percent of time defecating in or around streams
  amn.percentstream <- as.numeric(df.input$value[
    df.input$parameter == "Percent of animals in and around streams"]) / 100
  ## all landuse has stream access for Racoon
  ## bacteria production per animal
  amn.bac.prod  <- as.numeric(df.input$value[
    df.input$parameter == "Bacteria Production of animal per day (orgs/day)"])
  amn.SQLIM.factor  <- as.numeric(df.input$value[
    df.input$parameter == "SQOLIM multiplcation factor"])

  ##
  ### Calculations
  ### Populations
  pop.total   <- lu.habitatarea * amn.density
  pop.pasture   <- lu.pasture.area * amn.density
  pop.forest   <- lu.forest.area * amn.density
  pop.RAOCUT   <- lu.RAOCUT.area * amn.density
  pop.on.land     <- (1-amn.percentstream) * pop.total
  pop.on.land.pasture <- (1-amn.percentstream) * pop.pasture
  pop.on.land.forest <- (1-amn.percentstream) * pop.forest
  pop.on.land.RAOCUT     <- (1-amn.percentstream) * pop.RAOCUT
  pop.in.stream <- amn.percentstream * pop.total
  ### bacteria loads
  bac.total <- pop.total * amn.bac.prod
  bac.on.land  <- amn.bac.prod * pop.on.land
  bac.on.land.pasture  <- amn.bac.prod * pop.on.land.pasture
  bac.on.land.forest  <- amn.bac.prod * pop.on.land.forest
  bac.on.land.RAOCUT  <- amn.bac.prod * pop.on.land.RAOCUT
  bac.in.stream   <- amn.bac.prod * pop.in.stream
  ### accum values
  accum.pasture <- 0
  accum.forest  <- 0
  accum.RAOCUT  <- 0
  if(lu.pasture.area > 0) {
    accum.pasture  <- bac.on.land.pasture / lu.pasture.area
  }
  if(lu.forest.area > 0) {
    accum.forest  <- bac.on.land.forest / lu.forest.area
  }
  if(lu.RAOCUT.area > 0) {
    accum.RAOCUT  <- bac.on.land.RAOCUT / lu.RAOCUT.area
  }
  ##
  ## Assemble output data frame
  df.output <- data.frame(
    sub = chr.sub,
    Month=format(as.POSIXct(paste0("1967-",1:12,"-01")), format = "%b"),
    pop.total=pop.total,
    pop.on.land=pop.on.land,
    pop.in.stream=pop.in.stream,
    Bacteria.total=bac.total,
    Bacteria.on.land=bac.on.land,
    Bacteria.direct.to.stream=bac.in.stream,
    Accum.pasture=accum.pasture,
    Accum.forest=accum.forest,
    Accum.RAOCUT=accum.RAOCUT,
    Lim.pasture=amn.SQLIM.factor * accum.pasture,
    Lim.forest=amn.SQLIM.factor * accum.forest,
    Lim.RAOCUT=amn.SQLIM.factor * accum.RAOCUT,
    stringsAsFactors=FALSE)

  ##
  ### return results
  return(df.output)
}