Green Unified Scenarios: Digital Twinning
Part.2
This is the second of a series of posts on a framework and software we have built within the TreesAI project. In these posts, we would like to highlight our underlying thinking. In a nutshell, we aim to create a set of tools for practitioners and researchers so that they can design a new urban forest or explore the impact of an existing one with the ability to estimate each ecosystem service, such as air-pollution removal or flood risk reduction.
How to digitise “the” urban forest
In the last post, we explained how a digital twin of an urban forest could help better design, forecast, and manage the real forest. Now, let’s dive a bit more into the process of digitising an urban forest. Since models and agents are abstractions of reality, the key question would be: what should be particularly granular, and how can we do so? As every city has its own built environment and policies for tree care, computational models that are too generalistic are bound to fall short of capturing the geo-context of a specific site. Therefore, digital forests should embody “the” urban forest, not an overly generalised version of “a” forest. In order to make the model site-specific, we capture four underlying characteristics of an urban forest.
Above all, we should take into account that urban forests are located at particular physical locations where the soil structure, density of trees, sun exposure, and water access vary drastically. We name this specificity factor. It is always essential to include location-specific data because the different locations in different cities or even within the same city can have significant differences. For example, while comparing the average tree height and trunk size of maple trees of the same species and age, it is very likely to observe significant variations when we move from one city to another. Also, it would not be surprising to see substantial variations between the two parks in the same town.
Second, each urban forest may have different shapes and physiology. The species composition of a forest and its spatial distribution can make the forest respond to the same environment differently, thereby significantly affecting the outcomes of ecosystem services and also its resilience to external factors such as forest fires, tree pests and diseases. For example, while European Ash can capture more carbon than Cedar, a Cedar tree becomes more effective in stormwater alleviation when grown into maturity. Such heterogeneity is an inherent feature of complex systems. Furthermore, even initially homogeneous trees can gain heterogeneity throughout their life cycle. For instance, while they compete for canopy space and sun exposure, each tree’s dieback rate and recovery depend on its unique history and location.
Third, a tree is in constant interactions with its environment and other trees. These individual tree-level interactions lead to an emergence of forest-level growth patterns that might differ from a single tree. Due to specificity and heterogeneity, its growth process is dynamic depending on its size, age, and access to resources such as light, soil, water, or other factors. For example, in our simulations, we have seen that the maturity age of a forest can be significantly different from that of individual trees. A tree can reach its maturity at the age of 30, whereas to observe a forest reaching its maturity may take more than 100 years. Moreover, a mature forest accommodates an ecosystem with significant biological diversity, high carbon storage per m2, etc. Another emergent behaviour of the forest is its ability to accommodate a network fungus that enables the flow of essential materials and information.
Lastly, an urban forest is an open system and is exposed to external shocks such as invasive insects, fires, and frequent human interventions. Not only the material but immaterial external factors from humans such as policy interventions, transportation rules, and legal system can significantly affect the urban forest. Such externalities are essential and integral to the analysis of complex systems.
In the next post, we will explain how our framework addresses uncertainties and maintains transparency.
Lucidminds AI
