Satellite-based estimation of roughness lengths and displacement heights for wind resource modelling

Wind turbines in northern Europe are frequently placed in forests, which sets new wind resource modelling requirements. Accurate mapping of the land surface can be challenging at forested sites due to sudden transitions between patches with very different aerodynamic properties, e.g. tall trees, clearings, and lakes.

Tree growth and deforestation can lead to temporal changes of the forest. Global or pan-European land cover data sets fail to resolve these forest properties, aerial lidar campaigns are costly and infrequent, and manual digitization is labour-intensive and subjective. Here, we investigate the potential of using satellite observations to characterize the land surface in connection with wind energy flow modelling using the Wind Atlas Analysis and Application Program (WAsP).

Collocated maps of the land cover, tree height, and leaf area index (LAI) are generated based on observations from the Sentinel-1 and Sentinel-2 missions combined with the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). Three different forest canopy models are applied to convert these maps to roughness lengths and displacement heights.

We introduce new functionalities for WAsP, which can process detailed land cover maps containing both roughness lengths and displacement heights. Validation is carried out through cross-prediction analyses at eight well-instrumented sites in various landscapes where measurements at one mast are used to predict wind resources at another nearby mast.

The use of novel satellite-based input maps in combination with a canopy model leads to lower cross-prediction errors of the wind power density (rms Combining double low line 10.9 %-11.2 %) than using standard global or pan-European land cover data sets for land surface parameterization (rms Combining double low line 14.2 %-19.7 %).

Differences in the cross-predictions resulting from the three different canopy models are minor. The satellite-based maps show cross-prediction errors close to those obtained from aerial lidar scans and manually digitized maps. The results demonstrate the value of using detailed satellite-based land cover maps for micro-scale flow modelling.