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Iso-guarantee design curves

Source: R/iso-curves.R
rh_iso_curve.Rd

Builds the "design curve" data behind rh_plot_iso(): for each target guarantee level and each value of the x-parameter, it solves (via rh_size_for()) for the y-parameter that reaches that level. The result is a family of iso-guarantee curves relating two design levers (the third is fixed in base), the rainwater-harvesting analogue of a reservoir design chart.

Usage

rh_iso_curve(
  precip,
  base = list(),
  x,
  x_values,
  y,
  levels = c(80, 90, 95, 100),
  metric = "attendance_pct",
  by = NULL,
  by_values = NULL,
  tol = 0.01,
  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 (everything except vary).

x, y

The two parameters to relate (each "area", "capacity" or "demand"); x is the axis you set, y is solved for.

x_values

Numeric values of x.

levels

Target guarantee levels (default c(80, 90, 95, 100)).

metric

Guarantee metric: "attendance_pct" (default, volumetric) or "reliability_pct" (time-based).

by

Optional third parameter to vary as panels/colour (e.g. "demand").

by_values

Values of by (required if by is set).

tol

Search tolerance (in units of vary).

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 long data frame with columns named after x, y, plus level (and by if used). Unreachable points are NA. Axis names are stored in attr(, "x"), attr(, "y"), attr(, "by"), attr(, "metric").

Examples

iso <- rh_iso_curve(
  precip_pi$value, base = list(demand = 6.6, runoff = 0.85, efficiency = 1),
  x = "area", x_values = seq(1000, 6000, length.out = 8),
  y = "capacity", levels = c(80, 90, 100),
  dates = precip_pi$date, climatology = TRUE
)
head(iso)
#>       area  capacity level
#> 1 1000.000 1237.9323    80
#> 2 1714.286  773.2401    80
#> 3 2428.571  623.2971    80
#> 4 3142.857  490.6135    80
#> 5 3857.143  374.4151    80
#> 6 4571.429  278.0653    80