cf_quadrant: Calculate climate futures based on their quadrant

In nationalparkservice/rcf: Reproducible Climate Futures

Description

Designates climate futures of "Warm Wet", "Warm Dry", "Hot Wet", "Hot Dry" and "Central" and calculates mean values for each GCM. Will additionally calculate the most extreme of each quadrant if calculation method is "corner". Calculates summary of threshold values based off of selected summarization parameter and calculation method.

Usage

cf_quadrant(
  SiteID = "unnamed_site",
  data = NULL,
  future_year = 2040,
  past_years = c(1950, 2000),
  method = "quadrant",
  summarize_by = "year",
  directory = tempdir()
)

Arguments

SiteID	chosen name to use in file names, attributes, and directories. (character)
data	Default data set to use for .csv creation. Must be created prior to running function. Follow vignette for example data set creation (data frame)
future_year	year to center changes from historical data around. Defaults to 2040 (numeric)
past_years	years to base past data off of. Cannot be any earlier than 1950. Must be written as c(past_start, past_end). Defaults to 1950-2000 (numeric)
method	method for calculating resulting .csv. Options "quadrant" and "corner". "quadrant" will return a data frame in which all models are labeled in their respective quadrants "hot wet" "hot dry" "warm wet" "warm dry" and "central "corner" will do the same, but additionally return the model that represents the most extreme scenario in each quadrant. Defaults to quadrant.
summarize_by	how to summarize the data, options are "month", "season", "year". Defaults to year.
directory	where to save files to. Per CRAN guidelines, this defaults to a temporary directory and files created will be lost after R session ends. Specify a path to retain files.

Value

Two (2) .csv files: 1.SiteID_future_means_x.csv - future means of all models calculated on central year 2. SiteID_y_summary_x.csv - Summary of threshold values calculated based on method (x) and summary metric (y). Means are taken based on month/season/year and either all models in a quadrant for quadrant method, or most extreme model in each quadrant for corner method x: c = corner, q = quadrant y: month, season or year

Examples

## Not run: 
# Generate sample data

data <- data.frame(
date = sample(seq(as.Date('1950/01/01'), as.Date('2099/12/31'), by="day"), 1000),
yr = rep(c(1980, 2040, 1980, 2040, 1980, 2040, 1980, 2040, 1980, 2040, 1980, 2040, 1980,
           2040, 1980, 2040, 1980, 2040, 1980, 2040), each = 50),
gcm = rep(c("bcc-csm1-1.rcp45", "BNU-ESM.rcp45", "CanESM2.rcp85", "CCSM4.rcp45",
            "CSIRO-Mk3-6-0.rcp45"), each = 200),
precip = rnorm(1000),
tmin = rnorm(1000),
tmax = rnorm(1000),
rhmax = rnorm(1000),
rhmin = rnorm(1000),
tavg = rnorm(1000),
heat_index = rnorm(1000),
heat_index_ec = rnorm(1000),
heat_index_dan = rnorm(1000),
temp_over_95_pctl =  as.logical(sample(x = c("TRUE","FALSE"), size = 1000, replace = TRUE)),
temp_over_99_pctl =  as.logical(sample(x = c("TRUE","FALSE"), size = 1000, replace = TRUE)),
temp_over_95_pctl_length =  as.logical(sample(x = c("TRUE","FALSE"), size = 1000,
                                              replace = TRUE)),
temp_under_freeze =  as.logical(sample(x = c("TRUE","FALSE"), size = 1000, replace = TRUE)),
temp_under_freeze_length =  as.logical(sample(x = c("TRUE","FALSE"), size = 1000,
                                              replace = TRUE)),
temp_under_5_pctl =  as.logical(sample(x = c("TRUE","FALSE"), size = 1000, replace = TRUE)),
no_precip  =  as.logical(sample(x = c("TRUE","FALSE"), size = 1000, replace = TRUE)),
no_precip_length =  as.logical(sample(x = c("TRUE","FALSE"), size = 1000, replace = TRUE)),
precip_95_pctl =  as.logical(sample(x = c("TRUE","FALSE"), size = 1000, replace = TRUE)),
precip_99_pctl =  as.logical(sample(x = c("TRUE","FALSE"), size = 1000, replace = TRUE)),
precip_moderate =  as.logical(sample(x = c("TRUE","FALSE"), size = 1000, replace = TRUE)),
precip_heavy =  as.logical(sample(x = c("TRUE","FALSE"), size = 1000, replace = TRUE)),
freeze_thaw =  as.logical(sample(x = c("TRUE","FALSE"), size = 1000, replace = TRUE)),
gdd =  as.logical(sample(x = c("TRUE","FALSE"), size = 1000, replace = TRUE)),
gdd_count = rnorm(1000),
not_gdd_count = rnorm(1000),
frost = as.logical(sample(x = c("TRUE","FALSE"), size = 1000, replace = TRUE)),
grow_length = rnorm(1000),
units = rep("imperial", each = 1000)
)

cf_quadrant("SCBL", data = df, future_year = 2040, past_years = c(1950, 2000),
method = "corner", summarize_by = "year")

## End(Not run)

nationalparkservice/rcf documentation built on Dec. 22, 2021, 12:01 a.m.