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

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.

Risø national laboratory and the orgin of WAsP

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

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.

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.

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

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

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

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

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

Final roughness map LARGE area Example zoom in

Preparing for fast simulations Low res High res 20 km

WAsP output

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

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

Lidar scan of Aberfoyle

Orography

Max. Tree height was set to 35m

Max PAI = 6

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

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 ?

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

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