Skip to contents

Collision risk model, for a single species and one turbine scenario

Source: R/band_crm.R
band_crm.Rd

Core function of the Collision Risk Model (CRM). Calculates the expected number of in-flight collisions per month of a seabird species on a given offshore windfarm, for a choice of model options.

Calculations are equivalent to those performed on the original CRM spreadsheet, as per Band (2012), providing backward compatibility with the original outputs.

Usage

band_crm(
  model_options = c("1", "2", "3", "4"),
  flight_speed,
  body_lt,
  wing_span,
  flight_type,
  avoid_rt_basic = NULL,
  avoid_rt_ext = NULL,
  noct_activity,
  prop_crh_surv = NULL,
  dens_month,
  prop_upwind,
  site_fhd = NULL,
  gen_fhd = NULL,
  rotor_speed,
  rotor_radius,
  blade_width,
  blade_pitch,
  n_blades,
  hub_height,
  chord_prof = chord_prof_5MW,
  n_turbines,
  turb_oper_month,
  wf_width,
  wf_latitude,
  tidal_offset,
  lrg_arr_corr = TRUE,
  xinc = 0.05,
  yinc = 0.05,
  ...
)

Arguments

model_options

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

flight_speed

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

body_lt

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

wing_span

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

flight_type

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

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_activity

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

prop_crh_surv

The proportion of flights at collision risk height derived from site survey (\(Q_2R\)). Only required for model Option 1.

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.

prop_upwind

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

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.

rotor_speed

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

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.

n_blades

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

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.

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.

n_turbines

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

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.

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.

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.

...

Additional arguments to pass on when function is called in stoch_crm(), namely rotor_grids and wf_daynight_hrs_month.

Value

Returns the expected number of bird collisions per month, for each of the chosen CRM Options. Returned months are those shared between dens_month and turb_oper_month. Output format is specific to how the function is called:

  • data frame object, if called as a stand-alone function.

  • list object, if called inside stoch_crm().

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.

Validation and pre-processing of inputs

band_crm() requirements and behaviour are dependent on how it is called:

As a stand-alone function

  • All arguments are expected to be specified as describe above

  • Input validation and pre-processing are carried out.

Inside stoch_crm()

  • Assumes inputs have already been pre-processed and validated, and thence it skips those steps.

  • Additional arguments rotor_grids and wf_daynight_hrs_month need to be passed to the function. Under the stochastic context, these quantities can be calculated ahead of the simulation loop to maximize performance.

  • Furthermore, gen_fhd, site_fhd, dens_month and turb_oper_month can be provided as numeric vectors

Code revision and optimization

Core code performing Band calculations in Masden (2015) implementation was substantially re-factored, re-structured and streamlined to conform with conventional R packages requirements.

Furthermore, previous code underpinning key calculations for the extended model was replaced by an alternative approach, resulting in significant gains in computational speed over Masden's approach. This optimization is particularly beneficial under a stochastic context, when Band calculations are called repeatedly, potentially thousands of times. See generate_rotor_grids() for further details.

Examples

# ------------------------------------------------------
# Run with arbitrary parameter values, for illustration
# ------------------------------------------------------

# Setting a dataframe of parameters to draw from
params <- data.frame(
  flight_speed = 13.1,         # Flight speed in m/s
  body_lt = 0.85,              # Body length in m
  wing_span = 1.01,            # Wing span in m
  flight_type = "flapping",    # flapping or gliding flight
  avoid_rt_basic = 0.989,      # avoidance rate for option 1 and 2
  avoid_rt_ext = 0.981,        # extended avoidance rate for option 3 and 4
  noct_activity = 0.5,         # proportion of day birds are inactive
  prop_crh_surv = 0.13,        # proportion of birds at collision risk height (option 1 only)
  prop_upwind = 0.5,           # proportion of flights that are upwind
  rotor_speed = 15,            # rotor speed in m/s
  rotor_radius = 120,          # radius of turbine in m
  blade_width = 5,             # width of turbine blades at thickest point in m
  blade_pitch = 15,            # mean radius pitch in Radians
  n_blades = 3,                # total number of blades per turbine
  hub_height = 150,            # height of hub in m above HAT
  n_turbines = 100,            # number of turbines in the wind farm
  wf_width = 52,               # width across longest section of wind farm
  wf_latitude = 56,            # latitude of centroid of wind farm
  tidal_offset = 2.5,          # mean tidal offset from HAT of the wind farm
  lrg_arr_corr = TRUE          # apply a large array correction?
)

# Monthly bird densities
b_dens <- data.frame(
  month = month.abb,
  dens = runif(12, 0.8, 1.5)
)

# flight height distribution from Johnston et al
gen_fhd_dat <- Johnston_Flight_heights_SOSS %>%
  dplyr::filter(variable=="Gannet.est") %>%
  dplyr::select(height,prop)


# monthly operational time of the wind farm
turb_oper <- data.frame(
  month = month.abb,
  prop_oper = runif(12,0.5,0.8)
)


band_crm(
  model_options = c(1,2,3),
  flight_speed = params$flight_speed,
  body_lt = params$body_lt,
  wing_span = params$wing_span,
  flight_type = params$flight_type,
  avoid_rt_basic = params$avoid_rt_basic,
  avoid_rt_ext = params$avoid_rt_ext,
  noct_activity = params$noct_activity,
  prop_crh_surv = params$prop_crh_surv,
  dens_month = b_dens,
  prop_upwind = params$prop_upwind,
  gen_fhd = gen_fhd_dat,
  site_fhd = NULL,  # Option 4 only
  rotor_speed = params$rotor_speed,
  rotor_radius = params$rotor_radius,
  blade_width = params$blade_width,
  blade_pitch = params$blade_pitch,
  n_blades = params$n_blades,
  hub_height = params$hub_height,
  chord_prof = chord_prof_5MW,
  n_turbines = params$n_turbines,
  turb_oper_month = turb_oper,
  wf_width = params$wf_width,
  wf_latitude = params$wf_latitude,
  tidal_offset = params$tidal_offset,
  lrg_arr_corr = params$lrg_arr_corr
  )
#> # A tibble: 12 × 4
#>    month  opt1  opt2  opt3
#>    <chr> <dbl> <dbl> <dbl>
#>  1 Jan    68.9  17.8  7.27
#>  2 Feb    68.2  17.6  7.20
#>  3 Mar    72.6  18.7  7.66
#>  4 Apr    91.7  23.6  9.67
#>  5 May    74.5  19.2  7.86
#>  6 Jun    80.9  20.9  8.53
#>  7 Jul   136.   35.2 14.4 
#>  8 Aug   113.   29.2 11.9 
#>  9 Sep    95.7  24.7 10.1 
#> 10 Oct    63.4  16.3  6.69
#> 11 Nov    47.7  12.3  5.03
#> 12 Dec    44.4  11.4  4.68