Input validator
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"
)Nothing returned from this function
Character vector, the model options for calculating collision risk (see Details section below).
An integer. The number of iterations for the model simulation.
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).
Numeric value. The bird flying speed (\(v\)), in metres/sec.
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.
Numeric value. The length of the bird (\(L\)), in metres.
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.
Numeric value. The wingspan of the bird (\(W\)), in metres.
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.
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.
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.
A numeric value. The nocturnal flight activity level, expressed as a proportion of daytime activity levels (\(f_night\)).
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.
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.
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.
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.
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.
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.
A character string, either 'flapping' or 'gliding', indicating the species' characteristic flight type.
Numeric value between 0-1 giving the proportion of flights upwind - defaults to 0.5.
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) tools:::Rd_expr_doi("10.1111/1365-2664.12191") (see usage in Example Section below).
Required only for model Option 4, a data frame similar
to gen_fhd_boots, but for FHD estimates derived from site-specific
data.
An integer, the number of blades in rotor (\(b\)).
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.
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.
Numeric value. The radius of the rotor (\(R\)), in metres.
Numeric value, giving the maximum blade width, in metres.
Numeric value. The average blade pitch angle, the angle between the blade surface and the rotor plane (\(\gamma\)), in radians.
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.
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.
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.
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.
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.
Numeric value. The operational rotation speed, in revolutions/min.
Integer, the number of turbines on the wind farm (\(T\)).
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.
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.
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.
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.
Integer, the number of turbines on the windfarm.
Numeric value, the approximate longitudinal width of the wind farm, in kilometres (\(w\)).
A decimal value. The latitude of the centroid of the windfarm, in degrees.
A numeric value, the tidal offset, the difference between HAT and mean sea level, in metres.
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.
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.
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.
Integer, the random seed for random number generation, for analysis reproducibility.
Logical, print model run progress on the console?
Output format specification. Possible values are:
"draws": returns stochastic draws of collisions estimates (default value),
"summaries": returns summary statistics of collisions estimates.
Logical, whether to output summary statistics of values sampled for each stochastic model parameter.
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.
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.
A single row data frame with columns mean and sd.
The population estimate of the species expected to fly through the wind farm
area.
a character string specifying the parent function whose inputs are being checked:
"scrm": checks stoch_crm() inputs
"crm": checks band_crm() inputs
"mcrm": checks mig_stoch_crm() inputs
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")
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