15 minutes with Professor Paula Harrison, Principal Natural Capital Scientist at the UK Centre for Ecology and Hydrology. In the second of our three guest blogs on the same theme, Paula shares her insights on creating effective land use decision making tools.
I work with government and the academic community on a range of land use modelling projects. For instance the ERAMMP Integrated Modelling Platform brings together a range of models and helps the Welsh Government to test the likely impacts of policies on agriculture, land use and the natural environment. My academic work includes being part of the Food, Agriculture, Biodiversity, Land-Use, and Energy (FABLE) global consortium, which shares learning on the development of tools for exploring global and national pathways to sustainable land-use and food systems.
Working with government and academics is quite different. Although both involve collaboration, the co-design process is more intensive and iterative when working with government, and models are usually designed with specific policy goals in mind. My academic work has a broader purpose and is designed to be generally applicable for a wide range of stakeholders. Long-term funding is essential for co-production of effective tools for decision making as it provides more opportunities for iterative and longer-term collaborative work.
Effective land use decision making tools need to incorporate multiple models within flexible frameworks. Models try to represent a complex real-world situation. Although single sector models are useful (e.g. agricultural models, energy models), running them separately to inform land use decisions can lead to outputs that indicate very different magnitudes of change and even completely different directions of change, compared with running them together in an integrated system. If you want to avoid unintended consequences from decisions and maximise opportunities, you need to understand how different land uses compete and interact with each other. So, it’s important to link models together and this can be done in different ways. Integrated modelling platforms that are ‘hardwired’ with coded links are relatively easy to run once set-up but can be difficult to adapt. Alternatively, ‘soft coupling’ of models, where inputs and outputs are manually transferred between models, can be more time-consuming to run but are much more flexible and can be rapidly adapted to changing policy and decision-maker needs.
Automated coupling frameworks could really accelerate progress in land use modelling. Every project you work on has a different question or purpose that requires you to connect different models and there’s currently no easy, automated way of doing this. So you often have to start from scratch, with individual modellers working to match up their measurement units and temporal and spatial scales so that the models can communicate with each other. It would be a big step forwards to have a collaborative, cloud-based platform that provided a configurable, integrated model coupling framework. This would enable you to build bespoke integrated models for different purposes more easily, flexibly and robustly by coupling different individual models or ‘building blocks’ using automated linkages.
There’s no such thing as a perfect model, but they need to be fit for purpose. It’s important to use models in an exploratory ‘what if’ way, rather than seeking definitive answers, and there will always be a lot of uncertainty that you need to make decision-makers aware of. This is especially the case when you move beyond modelling biophysical processes to include social and behavioural factors in a model. Modelling tools should ideally be intuitive, accessible and adaptable, which is best achieved through co-designing and co-producing with the end user.