Like many other predators, golden eagles are adept at choosing the least difficult route. As they spread their wings and glide through the air, they often try to get the wind to push them upwards by making upward motions that allow them to glide and save energy while continuing to fly for longer periods of time.
Areas and conditions that allow such flying are also ideal for wind power generation, which increases the risk of collisions between eagles and other similar species and wind turbines.
Although wind energy has many environmental advantages and this country's clean energy production targets are dependent on its expansion, the process of adaptation with wildlife must be exercised. Eagles are particularly important in this context because of their conservation status and legal protection in the United States.
To ensure the safe coexistence of eagles and wind turbines, developers and operators are needed to visualize how these majestic animals can fly around wind farms. Studies are carried out in the USA on this subject.
The Stochastic Soaring Raptor Simulator (SSRS), a new software created at the National Renewable Energy Laboratory (NREL) with help from the U.S. Department of Energy's Office of Wind Energy Technologies, aims to predict the most likely long-haul flight paths. Upstream air currents are being mapped.
An SSRS user can select a location, then set a specific date, time and wind conditions. The model will then copy the possible paths the golden eagles would take as they passed the location. The model is publicly accessible on GitHub, where the software is stored.
SSRS goes beyond the capabilities of previous agent-based models (where raptors were “agents”) by including information about individual sites.
It explains the uncertainties in atmospheric conditions and predicts the raptor's decision making process.
The model includes topography features, climatic conditions, and turbine locations from publicly available data sources at the user's choice anywhere in the United States.
The model then identifies potential locations for positive airflows. Migrating golden eagles are added on the model, starting from random or evenly spaced points. The eagles' potential locations at any given time are displayed on a cumulative map created by SRSS by simulating and combining their flight paths.
It consists of maps showing the outputs of the model progressively, from aircraft detection to atmosphere/topography, orographic updraft, simulated eagle tracks, relative presence density to turbine locations and facility layout.
“A golden eagle's desire to reduce its energy use will influence its decision-making process.”
According to Rimple Sandhu, a postdoctoral fellow at NREL and lead author of the study published in Ecological Modeling, “the model uses trajectories that the eagle can take to create a possible map.
Thousands of flight paths can intersect with wind turbine locations, and these possible orbital maps, known as relative presence maps, show possible interactions. This assists users in evaluating turbine installation choices that pose less of a risk to birds.
The simulator was specially created to study changes in atmospheric conditions, eagle behavior and seasonal fluctuations, as well as changes between day and night.
The team, which included collaborators at Western EcoSystems Technology, Inc., the US Geological Survey, Conservation Science Global, West Virginia University, and Lafayette College, evaluated case studies on both seasonal and hourly scales.
He compared the simulated tracks to a real one-year-old eagle. These tracks included flight paths from GPS-tagged birds taken near three wind farms in Wyoming.
Although the model was two-dimensional and the observed data indicated vertical shifts, the overlap looked encouraging. For example, at a given power station, asset maps for migrating golden eagles clearly showed different routes for southward travel and northward migration.
According to observations, golden eagles usually fly at higher altitudes in summer than in spring and autumn.
According to Charles Tripp, one of the study's lead scientists and a machine learning researcher at NREL, these data are consistent with the stronger and more intense thermal upstreams that eagles use to soar at high altitudes during the summer months.
“Knowing how atmospheric conditions at the turbine scale affect flight patterns can help a wind farm operator decide what restraint measures to take. As we gain a better understanding of eagle flight patterns, we can improve restraint methods.
With the help of results from SRSS, wind farm operators can dynamically change turbine activity based on local conditions to avoid wildlife-turbine collisions in real time.
Operators can focus mitigation efforts at specific high-risk times of year by placing cameras or monitors during seasons when SRSS suggests eagles will be more active in the area, and turning off turbines when eagles are spotted.
Additionally, when building wind farms, developers may consider initially placing (or positioning) turbines in lower risk areas.