I guess it’s time for another modeling-oriented blog post, particularly since there are a lot of exciting modeling activities going on in iLand that I don’t want to keep from you. At this point they mostly concern disturbance modeling, and I want to talk about wind today, as I’m currently a visiting scientist at SLU in Sweden, learning a lot about the mechanisms of wind disturbance from Kristina Blennow. Wind generally is the most important/ detrimental agent in European forests. In the worst year on record, in 1999, 170 Mill. m³ were damaged by wind (mainly the storms Lothar and Martin. And to give you an idea of the economic importance of wind disturbance: The weather system Gudrun in January 2005 damaged approximately 75 Mill. m³ in southern Sweden and was estimated to have caused an economic damage of 2.4 Billion € in forestry alone.

Figure 1: Damage from the storm Gudrun (January 2005) in Sweden. The right picture shows approximately 1.3% of the wood damaged by Gudrun (~1 Mill. m³), stacked ~12m high at a local airfield. Image source: Jimmi Svensson.

So much for the motivation of addressing wind in simulation modeling. As already highlighted before, we really want to go the mechanistic modeling route in iLand, in order to being able to capture emerging properties of vegetation – climate – disturbance interactions. And mechanistic wind modeling has come a long way in recent years (see here and here for recent reviews). However, such concepts are rarely applied in landscape models (mainly due to limitations in the vegetation structure simulated by landscape models, and by computational demand of mechanistic wind simulations), which usually employ a more phenomenological, probabilistic approach. So the challenge we’re currently working on is to bring mechanisms of wind disturbance into the landscape modeling context.

I’ll not go into the details of our approach yet since we’re only about to test a v0.1 of the iLand wind disturbance module. I rather want to highlight two recent developments that I think are quite a big advancements with regard to our aims here. A particular strength of landscape modeling is the simulation of spatial processes in the landscape (think fire spread). These spread processes also occur for wind, as trees mostly start falling at edges, thus exposing a next cohort of trees, which are less adapted (since they have been sheltered) and fall even easier, which in turn exposes even more downwind trees… you get the idea – there is a spatial spread process going on also in a wind disturbance event, that is, however, mostly ignored in landscape modeling. Ken Byrne is to my knowledge the first who has taken on this important spread process in modeling, mechanistically calculating patterns of windthrow via a coupling of the models TASS and ForestGALES. Since iLand is by its very design quite efficient in handling spatial processes across the landscape we’re looking into ways of adopting such a spread algorithm also for iLand. The huge benefit of this would be that patterns and extent of wind damage would be simulated as emerging property of the dynamically simulated stand conditions (rather than being imposed in a “cookie-cutter” approach) – and would thus also be much more useful in the context of decision support for management.

The second recent development I want to highlight is also closely related to one of our favorite topics here at iLand, i.e. individual trees. Most mechanistic wind models to date have focused on average trees in homogeneous stands (and what the downsides of those assumptions are I have reiterated quite exhaustively, I think, so I’ll not go there again in this post). Recently, Hale and coworkers took the next step and investigated the wind loading of individual trees of different size and competitive position in a stand. They found two very interesting things: First, the relationship between tree size and turning moment per wind forcing is surprisingly stable. In other words, the way different trees in a stand respond to wind can be consistently explained by their size. But the imho even more innovative part of their study is that Hale and coworkers show that standard-issue competition indices (used in tree growth modeling) are also strongly related to tree response to wind. In other words, they not only describe the effect of competition well, but also are good proxies for the sheltering effect of local neighbors. This is something that we want to explore further in iLand, as simulating individual tree competition is a particular strength of our model, and employing it also in the context of local wind shelter might be an elegantly way to model how management - by changing vegetation (e.g. through thinning) - can influence disturbances. So big kudos to the colleagues out there doing great work on disturbance mechanisms, and stay tuned for more on modeling disturbances in iLand.