Zero-deficit guarantee frontier — rh_guarantee

Traces, over a range of one design variable, the guaranteed value of another: for each vary value it calls the matching sizing function (rh_guaranteed_demand(), rh_guaranteed_capacity() or rh_required_area()) to find the target that yields zero deficit. For example, vary the capacity and read the guaranteed demand for each, giving the demand-versus-capacity frontier.

Usage

rh_guarantee_curve(
  precip,
  base = list(),
  vary,
  values,
  target,
  method = "bisection",
  tol = 0.001,
  dates = NULL,
  climatology = FALSE
)

Arguments

precip: Numeric vector of daily precipitation (mm). If climatology = TRUE, it is first collapsed to its day-of-year mean (see dates).
base: Named list of the fixed rh_simulate() arguments needed by the chosen sizing function (everything except vary and target).
vary: Variable to range over: "capacity", "area" or "demand".
values: Numeric values of vary.
target: Variable to solve for (zero deficit): "demand", "capacity" or "area". Must differ from vary.
method, tol: Passed to the sizing function ("bisection"/"optimize"/"step").
dates: Optional date vector aligned with precip, required only when climatology = TRUE.
climatology: If TRUE, simulate on the day-of-year climatology (much faster) instead of the full series. Default FALSE.

Value

A data frame with two columns named after vary and target; the names are also stored in attr(, "vary") and attr(, "target"). Points where no feasible target exists are NA.

Examples

rh_guarantee_curve(
  precip_pi$value, base = list(area = 4170, runoff = 0.85, efficiency = 1),
  vary = "capacity", values = seq(100, 1000, by = 100), target = "demand",
  dates = precip_pi$date, climatology = TRUE
)
#>    capacity   demand
#> 1       100 2.150582
#> 2       200 3.055740
#> 3       300 3.781799
#> 4       400 4.422881
#> 5       500 5.014123
#> 6       600 5.536078
#> 7       700 6.049458
#> 8       800 6.546215
#> 9       900 7.013268
#> 10     1000 7.459210