WindChoir - A Tool for Supporting Localization of Wind Power by Representing Wind Resources, Their Extraction and Cumulative Environmental Impacts on a Map

Övrigt konferensbidrag, 2019

The WindChoir tool aims to enable stakeholders to compare locations for new wind power farms from both a wind power and an environmental point of view. Such a location comparison needs to both consider the distribution of the wind resource, the technology used, techno-economic factors and cumulative environmental impacts of wind farms and other significant stressor sources. The tool aims to enable comparisons between sites, between effects of existing stressors and wind power stressors as well as between various technologies.
The representation of the environmental effects, which is a main aim, is based on a relative and regional risk assessment model (Halpern et al., 2008; Landis & Wiegers, 1997) making it possible to make a map of cumulative environmental impacts of many stressors on many ecological endpoints using spatial information.
In addition to existing or potential wind power farms the WindChoir tool is setup to include multiple anthropogenic stressor sources, e.g. industry plants, roads and their traffic volumes, etc., which distributions and intensities are calculated based on available spatial data. Models are used to transform these source distributions into a set of relevant stressor distributions and intensities including noise, light, displacement, and chemical emissions.
The endpoints are defined at different ecological aggregation levels: habitats, representing groups of species occurring under particular biogeophysical conditions; specifically identified representative groups of species, e.g. raptors; or specified single key species such as golden eagle. Their distributions are modelled based on data from multiple sources including novel ground cover data (10mx10m pixels), elevation, and observations. Humans are also included as an ecological endpoint.
A matrix representing the sensitivity of each ecological endpoint to each stressor is developed based on ecological cause effect models and observation data.
In each pixel the distributions of stressor intensities and endpoints are matched and the cumulative impact is calculated with a simple algorithm using the sensitivity matrix.
The results can be displayed as maps or causality diagrams where the contribution from each stressor source to the impact on each endpoint can be seen. Statistics for evaluation of uncertainty can also be extracted. In this way the impact from of a wind farm can be set in relation to the impact from other existing anthropogenic stressor sources.
Locations of relevant technical, economical, and juridical factors such as wind resource, electricity grid, protected areas, etc., can be layered on the environmental impact map to provide a broader spectrum decision support.