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Input validator

Source: R/validate_inputs.R
validate_inputs.Rd

Input validator

Usage

validate_inputs(
  model_options,
  n_iter = NULL,
  flt_speed_pars = NULL,
  flight_speed = NULL,
  body_lt_pars = NULL,
  body_lt = NULL,
  wing_span_pars = NULL,
  wing_span = NULL,
  avoid_bsc_pars = NULL,
  avoid_rt_basic = NULL,
  avoid_ext_pars = NULL,
  avoid_rt_ext = NULL,
  noct_act_pars = NULL,
  noct_activity = NULL,
  prop_crh_pars = NULL,
  bird_dens_opt = NULL,
  bird_dens_dt = NULL,
  chord_prof = NULL,
  dens_month = NULL,
  turb_oper_month = NULL,
  flight_type = NULL,
  prop_upwind = NULL,
  gen_fhd_boots = NULL,
  site_fhd_boots = NULL,
  n_blades = NULL,
  air_gap_pars = NULL,
  rtr_radius_pars = NULL,
  rotor_radius = NULL,
  blade_width = NULL,
  blade_pitch = NULL,
  hub_height = NULL,
  bld_width_pars = NULL,
  rtn_pitch_opt = NULL,
  bld_pitch_pars = NULL,
  rtn_speed_pars = NULL,
  rotor_speed = NULL,
  n_turbines = NULL,
  windspd_pars = NULL,
  rtn_pitch_windspd_dt = NULL,
  trb_wind_avbl = NULL,
  trb_downtime_pars = NULL,
  wf_n_trbs = NULL,
  wf_width = NULL,
  wf_latitude = NULL,
  tidal_offset = NULL,
  gen_fhd = NULL,
  site_fhd = NULL,
  lrg_arr_corr = NULL,
  xinc = NULL,
  yinc = NULL,
  seed = NULL,
  verbose = NULL,
  out_format = NULL,
  out_sampled_pars = NULL,
  out_period = NULL,
  season_specs = NULL,
  popn_estim_pars = NULL,
  fn = "scrm"
)

Arguments

model_options

Character vector, the model options for calculating collision risk (see Details section below).

n_iter

An integer. The number of iterations for the model simulation.

flt_speed_pars

A single row data frame with columns mean and sd, the mean and standard deviation of the species flying speed, in metres/sec. Assumed to follow a Truncated Normal with lower bound at 0 (tnorm-lw0).

flight_speed

Numeric value. The bird flying speed (\(v\)), in metres/sec.

body_lt_pars

A single row data frame with columns mean and sd, the mean and standard deviation of the species body length, in metres. Assumed to follow a tnorm-lw0 distribution.

body_lt

Numeric value. The length of the bird (\(L\)), in metres.

wing_span_pars

A single row data frame with columns mean and sd, the mean and standard deviation of the species wingspan, in metres. Assumed to follow a tnorm-lw0 distribution.

wing_span

Numeric value. The wingspan of the bird (\(W\)), in metres.

avoid_bsc_pars, avoid_ext_pars

Single row data frames with columns mean and sd, the mean and standard deviation of the species avoidance rate to be used in the basic model (Options 1 and 2) and extended model (Options 3 and 4) calculations (see Details section). Avoidance rate expresses the probability that a bird flying on a collision course with a turbine will take evading action to avoid collision, and it is assumed to follow a Beta distribution.

avoid_rt_basic, avoid_rt_ext

Numeric values. The avoidance rate for, respectively, the basic model (i.e. required for model Options 1 and 2) and the extended model (i.e. required for Options 3 and 4). Avoidance rate expresses the probability that a bird flying on a collision course with a turbine will take evading action to avoid collision.

noct_act_pars

A single row data frame with columns mean and sd, The mean and standard deviation of the species nocturnal flight activity level, expressed as a proportion of daytime activity levels, and assumed to be Beta distributed.

noct_activity

A numeric value. The nocturnal flight activity level, expressed as a proportion of daytime activity levels (\(f_night\)).

prop_crh_pars

Required only for model Option 1, a single row data frame with columns mean and sd. The mean and standard deviation of the proportion of flights at collision risk height derived from site survey, assumed to be Beta distributed.

bird_dens_opt

Option for specifying the random sampling mechanism for bird densities:

  • "tnorm": Sampling of density estimates from a tnorm-lw0 distribution (default value),

  • "resample": Re-sample draws of bird density estimates (e.g. bootstrap samples),

  • "qtiles": Sampling from a set of quantile estimates of bird densities.

bird_dens_dt

A data frame with monthly estimates of bird density within the windfarm footprint, expressed as the number of daytime in-flight birds/km^2 per month. Data frame format requirements:

  • If bird_dens_opt = "tnorm", bird_dens_dt must contain the following columns:

    • month, (unique) month names,

    • mean, the mean number of birds in flight at any height per square kilometre in each month,

    • sd, idem, for standard deviation.

  • If bird_dens_opt = "resample", bird_dens_dt columns must be named as months (i.e. Jan, Feb, ...), each containing random samples of monthly density estimates.

  • If bird_dens_opt = "qtiles", bird_dens_dt must comply with:

    • First column named as p, giving reference probabilities,

    • Remaining columns named as months (i.e. Jan, Feb, ...), each giving the quantile estimates of bird density in a given month, for the reference probabilities in column p.

chord_prof

A data frame with the chord taper profile of the rotor blade. Function expects two named columns:

  • pp_radius, equidistant intervals of radius at bird passage point, as a proportion of rotor_radius, within the range \([0, 1]\).

  • chord, the chord width at pp_radius, as a proportion of blade_width.

Defaults to a generic profile for a typical modern 5MW turbine. See chord_prof_5MW() for details.

dens_month

Data frame, containing estimates of daytime in-flight bird densities per month within the windfarm footprint, in birds/km^2. It must contain the following named columns:

  • month, the month names.

  • dens, the number of birds in flight at any height per square kilometre in each month.

turb_oper_month

Data frame, holding the proportion of time during which turbines are operational per month. The following named column are expected:

  • month, the month names.

  • prop_oper, the proportion of time operating, per month.

flight_type

A character string, either 'flapping' or 'gliding', indicating the species' characteristic flight type.

prop_upwind

Numeric value between 0-1 giving the proportion of flights upwind - defaults to 0.5.

gen_fhd_boots

Required only for model Options 2 and 3, a data frame with bootstrap samples of flight height distributions (FHD) of the species derived from general (country/regional level) data. FHD provides relative frequency distribution of bird flights at 1-+ -metre height bands, starting from sea surface. The first column must be named as height, expressing the lower bound of the height band (thus it's first element must be 0). Each remaining column should provide a bootstrap sample of the proportion of bird flights at each height band, with no column naming requirements.

NOTE: generic_fhd_bootstraps is a list object with generic FHD bootstrap estimates for 25 seabird species from Johnson et al (2014) doi:10.1111/1365-2664.12191 (see usage in Example Section below).

site_fhd_boots

Required only for model Option 4, a data frame similar to gen_fhd_boots, but for FHD estimates derived from site-specific data.

n_blades

An integer, the number of blades in rotor (\(b\)).

air_gap_pars

A single row data frame with columns mean and sd, the mean and standard deviation of the tip clearance gap, in metres, i.e. the distance between the minimum rotor tip height and the highest astronomical tide (HAT). Assumed to follow a tnorm-lw0 distribution.

rtr_radius_pars

A single row data frame with columns mean and sd, the mean and standard deviation of the radius of the rotor, in metres. Assumed to follow a tnorm-lw0 distribution.

rotor_radius

Numeric value. The radius of the rotor (\(R\)), in metres.

blade_width

Numeric value, giving the maximum blade width, in metres.

blade_pitch

Numeric value. The average blade pitch angle, the angle between the blade surface and the rotor plane (\(\gamma\)), in radians.

hub_height

A numeric value, the height of the rotor hub (\(H\)), given by the sum of rotor radius and minimum blade clearance above the highest astronomical tide (HAT), in metres.

bld_width_pars

A single row data frame with columns mean and sd, the mean and standard deviation of the maximum blade width, in metres. Assumed to be tnorm-lw0 distribution.

rtn_pitch_opt

a character string, the option for specifying the sampling mechanism for rotation speed and blade pitch:

  • "probDist": sample rotation speed and blade pitch values from a tnorm-lw0 distribution (default value).

  • "windSpeedReltn": generate rotation speed and blade pitch values as a function of wind speed intensity.

bld_pitch_pars

Only required if rtn_pitch_opt = "probDist", a single row data frame with columns mean and sd, the mean and standard deviation of the blade pitch angle, i.e. the angle between the blade surface and the rotor plane, in degrees. Assumed to follow a tnorm-lw0 distribution.

rtn_speed_pars

Only required if rtn_pitch_opt = "probDist", a single row data frame with columns mean and sd, the mean and standard deviation of the operational rotation speed, in revolutions per minute. Assumed to follow a tnorm-lw0 distribution.

rotor_speed

Numeric value. The operational rotation speed, in revolutions/min.

n_turbines

Integer, the number of turbines on the wind farm (\(T\)).

windspd_pars

Only required if rtn_pitch_opt = "windSpeedReltn", a single row data frame with columns mean and sd, the mean and the standard deviation of wind speed at the windfarm site, in metres/sec. Assumed to follow a tnorm-lw0 distribution.

rtn_pitch_windspd_dt

Only required if rtn_pitch_opt = "windSpeedReltn", a data frame giving the relationship between wind speed, rotation speed and blade pitch values. It must contain the columns:

  • wind_speed, wind speed in m/s,

  • rtn_speed, rotation speed in rpm,

  • bld_pitch, blade pitch values in degrees.

trb_wind_avbl

A data frame with the monthly estimates of operational wind availability. It must contain the columns:

  • month, (unique) month names,

  • pctg, the percentage of time wind conditions allow for turbine operation per month.

trb_downtime_pars

A data frame with monthly estimates of maintenance downtime, assumed to follow a tnorm-lw0 distribution. It must contain the following columns:

  • month, (unique) month names,

  • mean, numeric, the mean percentage of time in each month when turbines are not operating due to maintenance,

  • sd, the standard deviation of monthly maintenance downtime.

wf_n_trbs

Integer, the number of turbines on the windfarm.

wf_width

Numeric value, the approximate longitudinal width of the wind farm, in kilometres (\(w\)).

wf_latitude

A decimal value. The latitude of the centroid of the windfarm, in degrees.

tidal_offset

A numeric value, the tidal offset, the difference between HAT and mean sea level, in metres.

gen_fhd, site_fhd

Data frame objects, with flight height distributions (fhd) of the species - the relative frequency distribution of bird flights at 1-metre height intervals from sea surface. Specifically:

  • gen_fhd, Data frame with the species' generic fhd derived from combining wider survey data. Only required for model Options 2 and 3

  • site_fhd, Data frame with the species' site-specific fhd derived from local survey data. Only required for model Option 4

Data frames must contain the following named columns:

  • height, integers representing height bands from sea surface, in metres. Function expects 0 as the first value, representing the 0-1m band.

  • prop, the proportion of flights at each height band.

lrg_arr_corr

Boolean value. If TRUE, the large array correction will be applied. This is a correction factor to account for the decay in bird density at later rows in wind farms with a large array of turbines.

yinc, xinc

numeric values, the increments along the y-axis and x-axis for numerical integration across segments of the rotor circle. Chosen values express proportion of rotor radius. By default these are set to 0.05, i.e. integration will be performed at a resolution of one twentieth of the rotor radius.

seed

Integer, the random seed for random number generation, for analysis reproducibility.

verbose

Logical, print model run progress on the console?

out_format

Output format specification. Possible values are:

  • "draws": returns stochastic draws of collisions estimates (default value),

  • "summaries": returns summary statistics of collisions estimates.

out_sampled_pars

Logical, whether to output summary statistics of values sampled for each stochastic model parameter.

out_period

Controls level of temporal aggregation of collision outputs. Possible values are:

  • "months": monthly collisions (default value),

  • "seasons": collisions per user-defined season,

  • "annum": total collisions over 12 months.

season_specs

Only required if out_period = "seasons", a data frame defining the seasons for aggregating over collision estimates. It must comprise the following columns:

  • season_id, (unique) season identifier,

  • start_month, name of the season's first month,

  • end_month, name of the season's last month.

popn_estim_pars

A single row data frame with columns mean and sd. The population estimate of the species expected to fly through the wind farm area.

fn

a character string specifying the parent function whose inputs are being checked:

Value

Nothing returned from this function

Examples

  validate_inputs(model_options=c(1),
               avoid_bsc_pars=data.frame(mean=0.99,sd=0.001),
               prop_crh_pars=data.frame(mean=0.01,sd=0.01),
               air_gap_pars = data.frame(mean=21,sd=0),
               rtr_radius_pars = data.frame(mean=100,sd=0),
               bld_pitch_pars = data.frame(mean=15,sd=0),
               rtn_pitch_opt = "probDist",
               rtn_speed_pars = data.frame(mean=14,sd=5),
               out_period = "months",
               lrg_arr_corr = TRUE,
               fn="scrm")