Atmospheric Modeling to Enable Prediction of Golden Eagle Interactions with Wind Power Plants

Eliot Quon, Michael Lawson, Charles Tripp, Caroline Draxl, Rimple Sandhu, Regis Thedin

Research output: NRELPresentation

Abstract

This panel will explore the use of technologies and methods that enhance our understanding of eagle behavior in and near wind energy facilities. The decreasing costs of tracking technologies and substantial computational capabilities are allowing for finer resolution and confidence of eagle observations and the modeling of such datasets increase our understanding of their movements and use of airspace and underlying landscapes. Behavior ecologists' intrinsic knowledge of eagle behavior can augment and further enhance inferences from technology-derived information alone, e.g., GPS, camera, radar, etc. The panel will discuss the state of science and, given the cost-effective tools available, what research objectives are emerging to better assess and reduce wind-eagle risk. The discussion will focus on the following areas: Integrating Behavioral and Quantitative Ecologist perspectives of bald eagle and golden eagle behavior and wind energy risk; Regional-scale vs. facility-scale investigations of bald eagle and golden eagle use of airspace. For example, avoidance behavior to assess risk at facility-scale, and eagle use of landscapes, e.g., territories, nesting, and life-cycle behavior; Bald eagle and golden eagle use of airspace and nexus with atmospheric flow dynamics of wind energy generation; GPS, biomonitoring, tracking camera, radar, and other tools/techniques for deepening the knowledge of eagle behavior in and around wind farms.
Original languageAmerican English
Number of pages20
StatePublished - 2020

Publication series

NamePresented at the 13th Wind Wildlife Research Meeting, 1-4 December 2020

NREL Publication Number

  • NREL/PR-5000-78464

Keywords

  • atmospheric modeling
  • behavioral modeling
  • complex terrain
  • golden eagles
  • high-fidelity modeling
  • machine learning

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