sample_parameters {stochLAB}

Parameter sampling whiz

Description

Generates the random samples of all the stochastic CRM parameters. For internal use.

Usage

sample_parameters(
  model_options,
  mod_mths,
  n_iter = 10,
  flt_speed_pars,
  body_lt_pars,
  wing_span_pars,
  avoid_bsc_pars,
  avoid_ext_pars,
  noct_act_pars,
  prop_crh_pars,
  bird_dens_opt = "tnorm",
  bird_dens_dt,
  gen_fhd_boots = NULL,
  site_fhd_boots = NULL,
  rtr_radius_pars,
  air_gap_pars,
  bld_width_pars,
  rtn_pitch_opt = "probDist",
  bld_pitch_pars,
  rtn_speed_pars,
  windspd_pars,
  rtn_pitch_windspd_dt,
  trb_wind_avbl,
  trb_downtime_pars,
  lrg_arr_corr
)

Arguments

model_options

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

mod_mths

character vector, the names of months under modelling

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Details

Collision risk can be calculated under 4 options, specified by model_options:

• Option 1 - Basic model with proportion at collision risk height derived from site survey (prop_crh_surv).

• Option 2 - Basic model with proportion at collision risk height derived from a generic flight height distribution (gen_fhd).

• Option 3 - Extended model using a generic flight height distribution (gen_fhd).

• Option 4 - Extended model using a site-specific flight height distribution (site_fhd).

Where,

• Basic model - assumes a uniform distribution of bird flights at collision risk height (i.e. above the minimum and below the maximum height of the rotor blade).

• Extended model - takes into account the distribution of bird flight heights at collision risk height.

Value

A list object with each element comprising sampled values of given CRM parameter

Examples

  bird_dens_dt <- data.frame(
    month = month.abb,
    mean = runif(12, 0.8, 1.5),
    sd = runif(12, 0.2, 0.3)
  )

 # wind availability
  trb_wind_avbl <- data.frame(
    month = month.abb,
    pctg = runif(12, 85, 98)
  )

  # maintenance downtime
  trb_downtime_pars <- data.frame(
    month = month.abb,
    mean = runif(12, 6, 10),
    sd = rep(2, 12))

  # Wind speed relationships
  wind_rtn_ptch <- data.frame(
    wind_speed = seq_len(30),
    rtn_speed = 10/(30:1),
    bld_pitch = c(rep(90, 4), rep(0, 8), 5:22)
    )


  bird_dens_opt <- "tnorm"
  ### extract and standardize month format from monthly data sets
  b_dens_mth <- switch (bird_dens_opt,
                        tnorm = bird_dens_dt$month,
                        resample = names(bird_dens_dt),
                        qtiles = names(bird_dens_dt)[names(bird_dens_dt) != "p"]
  ) %>% format_months()
  dwntm_mth <- format_months(trb_downtime_pars$month)
  windav_mth <- format_months(trb_wind_avbl$month)
  ### Set months to model: only those in common amongst monthly data sets
  mod_mths <- Reduce(intersect, list(b_dens_mth, dwntm_mth, windav_mth))
  ### Order chronologically
  mod_mths <- mod_mths[order(match(mod_mths, month.abb))]

  param_draws <- sample_parameters(
    model_options = c(1,2,3),
    n_iter = 10,
    mod_mths = mod_mths,
    flt_speed_pars = data.frame(mean=7.26,sd=1.5),
    body_lt_pars = data.frame(mean=0.39,sd=0.005),
    wing_span_pars = data.frame(mean=1.08,sd=0.04),
    avoid_bsc_pars = data.frame(mean=0.99,sd=0.001),
    avoid_ext_pars = data.frame(mean=0.96,sd=0.002),
    noct_act_pars = data.frame(mean=0.033,sd=0.005),
    prop_crh_pars = data.frame(mean=0.06,sd=0.009),
    bird_dens_opt = "tnorm",
    bird_dens_dt = bird_dens_dt,
    gen_fhd_boots = generic_fhd_bootstraps[[1]],
    site_fhd_boots = NULL,
    rtr_radius_pars = data.frame(mean=80,sd=0),
    air_gap_pars = data.frame(mean=36,sd=0),
    bld_width_pars = data.frame(mean=8,sd=0),
    rtn_pitch_opt = "windSpeedReltn",
    bld_pitch_pars = NULL,
    rtn_speed_pars = NULL,
    windspd_pars = data.frame(mean=7.74,sd=3),
    rtn_pitch_windspd_dt = wind_rtn_ptch,
    trb_wind_avbl = trb_wind_avbl,
    trb_downtime_pars = trb_downtime_pars,
    lrg_arr_corr = TRUE
    )