In previous posts, we’ve highlighted how we describe land cover in the Urb-ADAPT project for the purposes of modelling urban climate effects. These previous posts have been specific examples of what we might term our “theoretical framework”, by defining how we approach modelling and how we want to represent urban areas across EMRA, we are setting the parameters for addressing a more fundamental question: What data do we need to start planning for adaptation in urban areas?
We have summarised the data requirements to answer these questions below in the table.
By comparing the future levels of exposure / hazard / vulnerability to current we will then be well placed to start identifying adaptation requirements and options for local authorities across our region.
The answer to main question varies from place to place, but for our purposes, if we want to start planning for adaptation in urban areas in EMRA, we need to know:
- What will the climate be like in the future? (and how certain of this are we?)
- What will urban areas look like in the future? High-rise apartments? Semi-detached houses? Lots of street trees? Abundance of car parks? (and how can we predict this?)
- What will the population be like in the future? Young or old? Limited disposable income or wealthy? Well educated about environmental risks or uninformed? (and what characteristics are most important?)
- What will the oceans be like in the future? How will sea level rise affect wave height? (and how certain of this are we?
We can then begin to define data requirements for answering each of these questions. By combining these data in modelling environment we can identify areas in our region of interest that 1. Will experience a climate or coastal related hazard (and how certain of this we are) 2. Identify populations that will be exposed to these hazards and 3. How vulnerable the underlying population are to these hazards. This provides us with an understanding of where we will need to focus adaptation in the region and provide the basis for developing and costing solutions.