--- title: "Running stochLAB" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Running stochLAB} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ``` `{stochLAB}` is a tool to run Collision Risk Models (CRMs) for seabirds on offshore wind farms. ## Overview The `{stochLAB}` package is an adaptation of the [R code](https://data.marine.gov.scot/dataset/developing-avian-collision-risk-model-incorporate-variability-and-uncertainty-r-code-0) developed by [Masden (2015)](https://data.marine.gov.scot/dataset/developing-avian-collision-risk-model-incorporate-variability-and-uncertainty) to incorporate variability and uncertainty in the avian collision risk model originally developed by [Band (2012)](https://www.bto.org/sites/default/files/u28/downloads/Projects/Final_Report_SOSS02_Band1ModelGuidance.pdf). Code developed under `{stochLAB}` substantially re-factored and re-structured Masden's (heavily script-based) implementation into a user-friendly, streamlined, well documented and easily distributed tool. Furthermore, the package lays down the code infrastructure for easier incorporation of new functionality, e.g. extra parameter sampling features, model expansions, etc. In addition, previous code underpinning core calculations for the extended model has been replaced by an alternative approach, resulting in significant gains in computational speed over Masden's code. This optimization is particularly beneficial under a stochastic context, when core calculations are called repeatedly during simulations. For a more detailed overview type `?stochLAB`, once installed! ## Installation You can install the released version of stochLAB from [CRAN](https://CRAN.R-project.org) with: ``` r install.packages("stochLAB") ``` You can install the development version with: ```r # install.packages("devtools") devtools::install_github("HiDef-Aerial-Surveying/stochLAB") ``` ## Examples ### Simple example This is a basic example of running the stochastic collision model for one seabird species and one turbine/wind-farm scenario, with fictional input parameter data. ```r library(stochLAB) # ------------------------------------------------------ # Setting some of the required inputs upfront b_dens <- data.frame( month = month.abb, mean = runif(12, 0.8, 1.5), sd = runif(12, 0.2, 0.3)) # Generic FHD bootstraps for one species, from Johnson et al (2014) fhd_boots <- generic_fhd_bootstraps[[1]] # wind speed vs rotation speed vs pitch 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)) # wind availability windavb <- data.frame( month = month.abb, pctg = runif(12, 85, 98)) # maintenance downtime dwntm <- data.frame( month = month.abb, mean = runif(12, 6, 10), sd = rep(2, 12)) # seasons specification seas_dt <- data.frame( season_id = c("a", "b", "c"), start_month = c("Jan", "May", "Oct"), end_month = c("Apr", "Sep", "Dec")) # ---------------------------------------------------------- # Run stochastic CRM, treating rotor radius, air gap and # blade width as fixed parameters (i.e. not stochastic) stoch_crm( model_options = c(1, 2, 3), n_iter = 1000, 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 = b_dens, flight_type = "flapping", prop_upwind = 0.5, gen_fhd_boots = fhd_boots, n_blades = 3, rtr_radius_pars = data.frame(mean = 80, sd = 0), # sd = 0, rotor radius is fixed air_gap_pars = data.frame(mean = 36, sd = 0), # sd = 0, air gap is fixed bld_width_pars = data.frame(mean = 8, sd = 0), # sd = 0, blade width is fixed rtn_pitch_opt = "windSpeedReltn", windspd_pars = data.frame(mean = 7.74, sd = 3), rtn_pitch_windspd_dt = wind_rtn_ptch, trb_wind_avbl = windavb, trb_downtime_pars = dwntm, wf_n_trbs = 200, wf_width = 15, wf_latitude = 56.9, tidal_offset = 2.5, lrg_arr_corr = TRUE, verbose = TRUE, seed = 1234, out_format = "summaries", out_sampled_pars = TRUE, out_period = "seasons", season_specs = seas_dt, log_file = paste0(getwd(), "scrm_example.log") ) ``` ### Multiscenario example This is an example usage of `stoch_crm()` for multiple scenarios. The aim is two-fold: 1. Suggest how input parameter datasets used in the previous implementation can be reshaped to fit `stoch_crm()`'s interface. Suggested code is also relevant in the context of multiple scenarios applications, since the wide tabular structure of these datasets is likely the favoured format for users to compile input parameters under different scenarios. 2. Propose a functional programming framework to run `stoch_crm()` for multiple species and wind-farm/turbines features. Please note the example runs on fictional data. ```r # --------------------------------------------------------- # # ---- Reshaping into list-column data frames ---- # --------------------------------------------------------- # # # Here embracing list-columns tibbles, but lists could be used instead # --- bird features bird_pars <- bird_pars_wide_example %>% dplyr::relocate(Flight, .after = dplyr::last_col()) %>% tidyr::pivot_longer(AvoidanceBasic:Prop_CRH_ObsSD) %>% dplyr::mutate( par = dplyr::if_else(grepl("SD|sd|Sd", name), "sd", "mean"), feature = gsub("SD|sd|Sd","", name)) %>% dplyr::select(-name) %>% tidyr::pivot_wider(names_from = par, values_from = value) %>% tidyr::nest(pars = c(mean, sd)) %>% tidyr::pivot_wider(names_from = feature, values_from = pars) %>% tibble::add_column(prop_upwind = 0.5) # --- bird densities: provided as mean and sd Parameters for Truncated Normal lower # bounded at 0 dens_pars <- dens_tnorm_wide_example %>% tibble::add_column( dens_opt = rep("tnorm", nrow(.)), .after = 1) %>% tidyr::pivot_longer(Jan:DecSD) %>% dplyr::mutate( par = dplyr::if_else(grepl("SD|sd|Sd", name), "sd", "mean"), month = gsub("SD|sd|Sd","", name)) %>% dplyr::select(-name) %>% tidyr::pivot_wider(names_from = par, values_from = value) %>% tidyr::nest(mth_dens = c(month, mean, sd)) # --- FHD data from Johnson et al (2014) for the species under analysis gen_fhd_boots <- generic_fhd_bootstraps[bird_pars$Species] # --- seasons definitions (made up) season_dt <- list( Arctic_Tern = data.frame( season_id = c("breeding", "feeding", "migrating"), start_month = c("May", "Sep", "Jan"), end_month = c("Aug", "Dec", "Apr")), Black_headed_Gull = data.frame( season_id = c("breeding", "feeding", "migrating"), start_month = c("Jan", "May", "Oct"), end_month = c("Apr", "Sep", "Dec")), Black_legged_Kittiwake = data.frame( season_id = c("breeding", "feeding", "migrating"), start_month = c("Dec", "Mar", "Sep"), end_month = c("Feb", "Aug", "Nov"))) # --- turbine parameters ## address operation parameters first trb_opr_pars <- turb_pars_wide_example %>% dplyr::select(TurbineModel, JanOp:DecOpSD) %>% tidyr::pivot_longer(JanOp:DecOpSD) %>% dplyr::mutate( month = substr(name, 1, 3), par = dplyr::case_when( grepl("SD|sd|Sd", name) ~ "sd", grepl("Mean|MEAN|mean", name) ~ "mean", TRUE ~ "pctg" )) %>% dplyr::select(-name) %>% tidyr::pivot_wider(names_from = par, values_from = value) %>% tidyr::nest( wind_avbl = c(month, pctg), trb_dwntm = c(month, mean, sd)) ## address turbine features and subsequently merge operation parameters trb_pars <- turb_pars_wide_example %>% dplyr::select(TurbineModel:windSpeedSD ) %>% dplyr::relocate(RotorSpeedAndPitch_SimOption, .after = 1) %>% tidyr::pivot_longer(RotorRadius:windSpeedSD) %>% dplyr::mutate( par = dplyr::if_else(grepl("SD|sd|Sd", name), "sd", "mean"), feature = gsub("(SD|sd|Sd)|(Mean|MEAN|mean)","", name) ) %>% dplyr::select(-name) %>% tidyr::pivot_wider(names_from = par, values_from = value) %>% tidyr::nest(pars = c(mean, sd)) %>% tidyr::pivot_wider(names_from = feature, values_from = pars) %>% dplyr::left_join(., trb_opr_pars) # --- windspeed, rotation speed and blade pitch relationship wndspd_rtn_ptch_example # --- windfarm parameters wf_pars <- data.frame( wf_id = c("wf_1", "wf_2"), n_turbs = c(200, 400), wf_width = c(4, 10), wf_lat = c(55.8, 55.0), td_off = c(2.5, 2), large_array_corr = c(FALSE, TRUE) ) # -------------------------------------------------------------- # # ---- Run stoch_crm() for multiple scenarios ---- # -------------------------------------------------------------- # # --- Set up scenario combinations scenarios_specs <- tidyr::expand_grid( spp = bird_pars$Species, turb_id = trb_pars$TurbineModel, wf_id = wf_pars$wf_id) %>% tibble::add_column( scenario_id = paste0("scenario_", 1:nrow(.)), .before = 1) # --- Set up progress bar for the upcoming iterative mapping step pb <- progress::progress_bar$new( format = "Running Scenario: :what [:bar] :percent eta: :eta", width = 100, total = nrow(scenarios_specs)) # --- Map stoch_crm() to each scenario specification via purrr::pmap outputs <- scenarios_specs %>% purrr::pmap(function(scenario_id, spp, turb_id, wf_id, ...){ pb$tick(tokens = list(what = scenario_id)) # params for current species c_spec <- bird_pars %>% dplyr::filter(Species == {{spp}}) # {{}} to avoid issues with data masking # density for current species c_dens <- dens_pars %>% dplyr::filter(Species == {{spp}}) # params for current turbine scenario c_turb <- trb_pars %>% dplyr::filter(TurbineModel == {{turb_id}}) # params for current windfarm scenario c_wf <- wf_pars %>% dplyr::filter(wf_id == {{wf_id}}) # inputs in list-columns need to be unlisted, either via `unlist()` or # indexing `[[1]]` # switching off `verbose`, otherwise console will be # cramped with log messages stoch_crm( model_options = c(1, 2, 3), n_iter = 1000, flt_speed_pars = c_spec$Flight_Speed[[1]], body_lt_pars = c_spec$Body_Length[[1]], wing_span_pars = c_spec$Wingspan[[1]], avoid_bsc_pars = c_spec$AvoidanceBasic[[1]], avoid_ext_pars = c_spec$AvoidanceExtended[[1]], noct_act_pars = c_spec$Nocturnal_Activity[[1]], prop_crh_pars = c_spec$Prop_CRH_Obs[[1]], bird_dens_opt = c_dens$dens_opt, bird_dens_dt = c_dens$mth_dens[[1]], flight_type = c_spec$Flight, prop_upwind = c_spec$prop_upwind, gen_fhd_boots = gen_fhd_boots[[spp]], n_blades = c_turb$Blades, rtr_radius_pars = c_turb$RotorRadius[[1]], air_gap_pars = c_turb$HubHeightAdd[[1]], bld_width_pars = c_turb$BladeWidth[[1]], rtn_pitch_opt = c_turb$RotorSpeedAndPitch_SimOption, bld_pitch_pars = c_turb$Pitch[[1]], rtn_speed_pars = c_turb$RotationSpeed[[1]], windspd_pars = c_turb$windSpeed[[1]], rtn_pitch_windspd_dt = wndspd_rtn_ptch_example, trb_wind_avbl = c_turb$wind_avbl[[1]], trb_downtime_pars = c_turb$trb_dwntm[[1]], wf_n_trbs = c_wf$n_turbs, wf_width = c_wf$wf_width, wf_latitude = c_wf$wf_lat, tidal_offset = c_wf$td_off, lrg_arr_corr = c_wf$large_array_corr, verbose = FALSE, seed = 1234, out_format = "summaries", out_sampled_pars = FALSE, out_period = "seasons", season_specs = season_dt[[spp]], log_file = NULL ) }) # --- close progress bar pb$terminate() # --- identify elements of output list names(outputs) <- scenarios_specs$scenario_id outputs ``` ### Band model example This is an example usage of `band_crm()`. This is for a single species and single set of turbine parameters. This replicates the Band (2012) worksheet. The `stoch_crm()` function wraps around this function, where `band_crm()` acts in essence as a single draw of `stoch_crm()`. Please note the example runs on fictional data. ```r # ------------------------------------------------------ # Run with arbitrary parameter values, for illustration # ------------------------------------------------------ # Setting a dataframe of parameters to draw from params <- data.frame( flight_speed = 13.1, # Flight speed in m/s body_lt = 0.85, # Body length in m wing_span = 1.01, # Wing span in m flight_type = "flapping", # flapping or gliding flight avoid_rt_basic = 0.989, # avoidance rate for option 1 and 2 avoid_rt_ext = 0.981, # extended avoidance rate for option 3 and 4 noct_activity = 0.5, # proportion of day birds are inactive prop_crh_surv = 0.13, # proportion of birds at collision risk height (option 1 only) prop_upwind = 0.5, # proportion of flights that are upwind rotor_speed = 15, # rotor speed in m/s rotor_radius = 120, # radius of turbine in m blade_width = 5, # width of turbine blades at thickest point in m blade_pitch = 15, # mean radius pitch in Radians n_blades = 3, # total number of blades per turbine hub_height = 150, # height of hub in m above HAT n_turbines = 100, # number of turbines in the wind farm wf_width = 52, # width across longest section of wind farm wf_latitude = 56, # latitude of centroid of wind farm tidal_offset = 2.5, # mean tidal offset from HAT of the wind farm lrg_arr_corr = TRUE # apply a large array correction? ) # Monthly bird densities b_dens <- data.frame( month = month.abb, dens = runif(12, 0.8, 1.5) ) # flight height distribution from Johnston et al gen_fhd_dat <- data.frame( height = Johnston_Flight_heights_SOSS$Height, prop = Johnston_Flight_heights_SOSS$Gannet.est ) # monthly operational time of the wind farm turb_oper <- data.frame( month = month.abb, prop_oper = runif(12,0.5,0.8) ) stochLAB::band_crm( model_options = c(1,2,3), flight_speed = params$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 ) ```