The goal of SSRS is to predict movements of soaring raptors (such as Golden Eagles) for a given wind conditions with the aim of determining potential negative conflict between soaring raptors and wind turbines. SSRS uses a stochastic agent-based model for predicting raptor movements through an orographic updraft field estimated using the spatially varying wind conditions and ground features (altitude, slope, aspect). SSRS can be applied to any rectangular region within the US without the need for any eagle-centric or atmosphere-related data collection efforts, using only the publicly available data sources. SSRS implements and extends the capability of the fluid-flow model from 'Brandes, D., & Ombalski, D. (2004). Modelling raptor migration pathways using a fluid-flow analogy. The Journal of Raptor Research, 38(3), 195-207.' More details can be found in the publication:
Rimple Sandhu, Charles Tripp, Eliot Quon, Regis Thedin, Michael Lawson, David Brandes, Christopher J. Farmer, Tricia A. Miller, Caroline Draxl, Paula Doubrawa, Lindy Williams, Adam E. Duerr, Melissa A. Braham, Todd Katzner, Stochastic agent-based model for predicting turbine-scale raptor movements during updraft-subsidized directional flights, Ecological Modelling, Volume 466, 109876, 2022.
SSRS uses the following publically available data sources:
- USGS's 3D Elevation Program (3DEP) dataset for terrain altitude, slope and aspect at a spatial resolution of 10 meters within the US.
- USGS's United States Wind Turbine Database (USWTDB) for up-to-date turbine locations within the US.
- NREL's Wind ToolKit (WTK) dataset for atmospheric conditions such as wind speed and direction at 1-hour temporal resolution and 2 km spatial resolution within the US.
SSRS operates under three modes:
- Uniform: Uses uniform wind speed and direction across the target region.
- Snapshot: Uses wind conditions for a specific time imported from WTK dataset.
- Seasonal: Uses wind conditions randomly sampled from a range of dates or months or time of day from the WTK dataset.
Clone the GitHub repository on local machine, cd into the SSRS directory and using conda virtual environments from Anaconda, do:
conda env create -f environment.yml
conda activate ssrs_env
pip install .For editable install (for development purpose), do
conda env create -f environment.yml
conda activate ssrs_env
pip install -e .For running conda environment ssrs_env in Jupyter Notebook (Comes with Anaconda),
conda install ipykernel
ipython kernel install --user --name=ssrs_envWithout Anaconda (requires python>=3.8 and pip>21.3):
pip install git+https://github.com/NREL/SSRS.git#egg=ssrsFor SSRS to access NREL's WTK dataset in the snapshot mode, need to get an API key from https://developer.nrel.gov/signup/ and save it in .hscfg file located in the working directory. Both examples/ and notebooks/ directories contain a sample .hscfg_need_api_key file. Make sure to rename this file as .hscfg after inserting your API key in this file.
The Jupyter notebooks in notebooks/ and python scripts in examples/ show the usage of SSRS for a given region. For instance, ssrs simulation in uniform mode for a 60 km by 50 km region in Wyoming and simulating 1000 eagles travelling north can be implemented using the following code:
config_wy_uniform = Config(
run_name='run_wy',
southwest_lonlat=(-106.21, 42.78),
region_width_km=(60., 50.),
resolution=100.,
sim_mode='uniform',
uniform_winddirn=270.,
uniform_windspeed=10.,
track_direction=0.,
track_count = 1000,
track_start_region=(5, 55, 1, 2)
)
sim = Simulator(config_wy_uniform)
sim.simulate_tracks()
sim.plot_terrain_elevation(show=True)
sim.plot_updrafts(show=True)
sim.plot_simulated_tracks(show=True)
sim.plot_presence_map(show=True)This will produce the following figures:
Ground elevation and turbine locations:
Orographic updrafts:
1000 simulated tracks travelling towards north:
Relative eagle presence density
SSRS settings can be changed through a set of parameters defined using ssrs.Config attribute. The default setting can be viewed through following code:
from ssrs import Config
print(Config())Here is a description of the parameters available to the users to vary:
run_name: str = 'default' # name of this run, determines directory names
out_dir: str = os.path.join(os.path.abspath(os.path.curdir), 'output')
max_cores: int = 8 # maximum number of cores to use
sim_seed: int = -1 # random number seed
sim_mode: str = 'uniform' # snapshot, seasonal, uniform
print_verbose: bool = False # if want to print verboseParameters for setting up the region:
southwest_lonlat: Tuple[float, float] = (-106.21, 42.78)
projected_crs: str = 'ESRI:102008' # ESRI, EPSG, PROJ4 or WKT string
region_width_km: Tuple[float, float] = (60., 50.)
resolution: int = 100. # desired terrain resolution (meters)Parameters for setting up the uniform mode:
uniform_winddirn: float = 270. # northerly = 0., easterly = 90, westerly=270
uniform_windspeed: float = 10. # uniform wind speed in m/sParameters for setting up the snapshot mode:
snapshot_datetime: Tuple[int, int, int, int] = (2010, 6, 17, 13)Parameters for setting up the seasonal mode:
seasonal_start: Tuple[int, int] = (3, 20) # start of season (month, day)
seasonal_end: Tuple[int, int] = (5, 15) # end of season (month, day)
seasonal_timeofday: str = 'daytime' # morning, afternoon, evening, daytime
seasonal_count: int = 8 # number of seasonal updraft computationsParameters for importing data from NREL's WTK dataset:
wtk_source: str = 'AWS' # 'EAGLE', 'AWS', 'EAGLE_LED'
wtk_orographic_height: int = 100 # WTK wind conditions at this height
wtk_thermal_height: int = 100 # WTK pressure, temperature, at this height
wtk_interp_type: str = 'linear' # 'nearest' 'linear' 'cubic'Parameters for simulating tracks:
track_direction: str = 0. # movement direction measured clockwise from north
track_count: str = 1000 # number of simulated eagle tracks
track_start_region: Tuple[float, float, float, float] = (5, 55, 1, 2) # xmin, xmax, ymin, ymax [km]
track_start_type: str = 'random' # uniform, random
track_stochastic_nu: float = 1. # scaling of move probs, 0 = random walk
track_dirn_restrict: int = 3 # restrict within 45 deg of this previous movesParameters for plotting:
fig_height: float = 6. # height of the figure window
fig_dpi: int = 200 # increase this to get finer plots
turbine_minimum_hubheight: float = 50. # for plotting turbine locations
turbine_mrkr_styles = ('1k', '2k', '3k', '4k',
'+k', 'xk', '*k', '.k', 'ok')
turbine_mrkr_size: float = 3. # marker size for plotting turbines- Rimple Sandhu, National Renewable Energy Laboratory rimple.sandhu@nrel.com
- Charles Tripp, National Renewable Energy Laboratory, charles.tripp@nrel.gov
- Eliot Quon, National Renewable Energy Laboratory
- Regis Thedin, National Renewable Energy Laboratory
- Lindy Williams, National Renewable Energy Laboratory
- Paula Doubrawa, National Renewable Energy Laboratory
- Caroline Draxl, National Renewable Energy Laboratory
- Mike Lawson, National Renewable Energy Laboratory
Sandhu, Rimple, Tripp, Charles, Quon, Eliot, Thedin, Regis, Williams, Lindy, Doubrawa, Paula, Draxl, Caroline, and Lawson, Mike. SSRS (Stochastic Soaring Raptor Simulator). Computer Software. https://github.com/NREL/SSRS. USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office. 18 Oct. 2021. Web. doi:10.11578/dc.20210903.2.