Environmental Engineering Reference
In-Depth Information
In the United States, the preferred data set is the National Land Cover Database
(NLCD 2001), a 30-m resolution data set derived from Landsat Thematic Mapper
imagery. A similar data set known as EarthSat GeoCover is available for purchase in
other regions of the world from MDA Federal, Inc. Many countries have home-grown
land cover data sets, and Europe is covered by the Coordination for Information on
the Environment (CORINE) system.
In most cases, the land cover classifications must be converted to roughness values
(in meters) to be used in numerical wind flow modeling. There is no universally
accepted roughness conversion system. Table 13-1 presents the ranges of values used
by AWS Truepower.
For more precise land cover information, some private companies (e.g., Intermap
Technologies) offer high resolution surface models that include features such as build-
ings, vegetation, and roads, in addition to terrain elevation data.
13.2.3 Mast Number and Placement
To achieve the standards of accuracy required for energy production estimates for
utility-scale wind projects, all wind flow modeling must be anchored in high quality
observations from the project area. A minimum of one mast, equipped and installed to
the specifications described in this topic, is recommended, although a remote sensing
system may serve as the primary observational platform in some cases.
Wind resource data should be collected at locations representing the full range of
wind conditions likely to be encountered by the wind turbines in the project. Where this
criterion is not met, the uncertainty in the spatial modeling increases substantially. This
criterion is sometimes translated into distance, as in the guideline that no turbine should
be placed farther than 1-3 km from a met mast in complex terrain (Table 3-2). But
distance is only one factor to consider; differences in elevation, topographic exposure,
slope, and aspect (angle of slope with respect to north) may also be important.
In general, the greater the number of masts deployed, the smaller the uncertainty
in the predicted resource. However, this is true only if the masts are well distributed
throughout the proposed turbine array. If the masts are unevenly distributed, then the
benefit of the multiple masts is reduced. In the worst case, if the masts are clumped in
one area while the turbines are in a different area, then the additional masts provide
little, if any, benefit. Examples of preferred and poor mast placement are provided in
Figure 13-7. Uncertainty is discussed further in Chapter 15.
Table 13-1. Roughness ranges for typical land use/land cover
categories. Values may vary with geographic location
Land cover type
Roughness range, m
Water
0.001
Urban/developed area
0.3-0.75
Forest
0.9-1.125
Wetland
0.15-0.66
Shrub land
0.1-0.2
Cropland
0.03-0.07
Source : AWS Truepower.
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