1. Ebba Dellwik, Duncan Heathfield, Barry Gardiner
WAsP-ForestGALES: a merged tool for improved forest wind damage prediction
Ebba Dellwik, Duncan Heathfield, Barry Gardiner
Most of you know Barry,
WORLD
INABOX

2. Starting point
Currently, the ForestGALES model does not include a spatially varying wind field. We insert WAsP wind field predictions into ForestGALES and propose an automated way of doing this.
A simple flow model (such as WAsP) is better than no flow model.

3. Risø national laboratory and the orgin of WAsP

4. Risø national laboratory and the origin of WAsP
In 2017, WAsP is still the most widely
used siting tool for wind turbines.

5. Key model properties
WAsP: Surface parametrization Elevation map Roughness map for large area. Displacement height map (optional) Wind input Observations fit to a Weibull distribution Output Prediction based on a linearized model (IBZ) for terrain elevation and a parametrized non-linear roughness change model. Gridded wind field “resource grid”
ForestGALES: Stand parametrization Compartments where the forest properties are constant, added by user. Some of the forest parameters are recalculated to {z0,d} using Raupach (1994, 1995). Wind input DAMS score based on Wind Zone and local elevation or Weibull distribution from data. Output Wind risk assessment
Wind Zone local elevation -> Weibull parameters (Weibull A) from which probability of strong winds is calculated.

6. Example case: Aberfoyle in Southern Scotland
Intention: Predict a map of time until first damage for a small area of Aberfoyle forest and discuss how the map would have looked without the coupling.

7. Recipe for “TOMBERONT”:
Raw ingredients
Map of DAMS windiness zones for UK (Source: Forest Research)
Ordnance Survey explorer elevation contour data for tile NS, NT (Source: OS)
Aerodynamic roughness data for a 50km square area around Aberfoyle (Source: GWA Map Server for Online WAsP)
Forest stand data (polygons and properties) for Aberfoyle area (Source: Juan Suarez)
Method
Make a WasP *.tab file representing DAMS windiness score, and make a WasP *.lib wind atlas file from the DAMS tab file
Make a WAsP-optimised elevation vector map
Use ForestGales to get roughness lengths from forest map
Blend forest-derived roughness map with background roughness map
Construct WAsP workspace with small resource grid, and run
Get Weibull A and K for sector with maximum speed for each node
Use ForestGales to get predicted return period for each node

8. Work flow: trial coupling at Aberfoyle
WAsP: Surface parametrization Elevation map Roughness map Displacement height map Wind data input Weibull distribution of observed wind fields. Output Gridded wind field “resource grid” with Weibull parameters for each grid point
ForestGALES: Stand parametrization Compartments where the forest properties are constant added by user. Some of the forest parameters are recalculated to {z0,d} using Raupach (1994, 1995). Wind DAMS score based on Wind Zone and local elevation. Output Wind risk assessment

9. Making the WAsP roughness map
Forest area where we have use stand data subcompartments. Prediction area (small pink area)

10. Addition of Corinne roughness
Square is huge area, add first picture,

11. Comparison of roughness values from stand data and Corinne map
Z0 forest = 1.2m
Corinne
Stand data
Juan Suarez,
Raupach (1994)

12. Final roughness map
LARGE area
Example zoom in

13. Preparing for fast simulations
Low res
High res
20 km

14. WAsP output

15. Forest risk map, ForestGALES
Increasing risk
Recurrence interval, return period. 200 years (or more) event to turn the forest over.

16. Other possible recipes
Using WAsP CFD, WAsP Engineering instead of WASP IBZ (the traditional linear orography model + nonlinear roughness change model)
Using a global extreme wind climate from WAsP Engineering
Using Gumbel distribution instead of Weibull
Add the displacement height to the elevation map in WAsP
Use airborne lidar data to estimate roughness + displacement height values

17. Lidar scan of Aberfoyle

18. Orography

19. Max. Tree height was set to 35m

20. Max PAI = 6

21. Roughness (h/10), could be adjusted to different forest height fraction.
60m resolution
300m resolution

22. Conclusions It seems possible to integrate ForestGALES with WAsP.
The integrated models provide more detailed spatial information on wind risk.
There are several possible ways of improvement.
Next step: Automated beta-version testing by expert users after September.
Anybody interested ?

23. Thank you for listening. Contact: ebde@dtu
Thank you for listening! Contact: Read more about wasp:
200 years
100 years
50 years
0 years

24. Scan density (first reflections)
Empty square
corresponds to
half-empty .las file.