Coastal Vulnerability sea level rise information

I am been using coastal vulnerability model and have some question about the inclusion of sea level rise (slr) trends in the model.
I already have done some runs, and would like to include some sea level rise scenarios within my analysis. I am been using the RCP trends for sea level rise in my area of interest (e.g. 0.2m of project slr for the year 2065, equates to 10 mm/year to the 20 year period studied).
The problem I have is that running with the slr information, the vulnerability index is lower when comparing with the run without slr information, without changing anything else!

As my understanding the vulnerability index would be higher, given that the exposure equation is a sum of the different ranks, but I didn’t find any place where to rank slr, only to include the projected trend (mm/year) within the shapefile, as referred in the manual.
Where I could making some mistake? I already have a shapefile with trend column, but nowhere to rank this input.

Thank you for helping!

Hi Jacinto, thanks for posting. Dealing with SLR can be tricky in this model, as it tends to be somewhat different than the other variables. The interesting variation of SLR is usually the variation over time (across scenarios) as opposed to variation across space (within one scenario). Most of the other variables, and the model as a whole, are designed to show relative differences across space.

The model handles the ranking of the SLR values in a similar way that it ranks other continuous variables, such as relief or surge potential (see Table 4.1 of the User’s Guide and How it Works: Sea level change). That may not be useful at all if your entire area of interest is subject to the same SLR trend. If the trend varies across space in your AOI, then it might be useful.

So, if you wish to compare SLR scenarios where the SLR trend for the whole region changes from one scenario to another, you might do this calculation manually after running the model. For example, you could open the coastal_exposure.csv output and create a new R_slr column with rank values of 3 for a baseline run, and then a column with ranks of 4 or 5 for a scenario. And you could use all the columns prefixed with R_, to re-calculate the exposure index using equation #1 in the user’s guide.

Finally, the exposure equation is a a geometric mean, rather than a sum, so adding another variable to the equation will not necessarily yield a larger exposure value.

Thank you very much for your input, Dave. It was really helpful.
It is true that for my study region I just have overall trends for each given scenarios and not differences within scenarios between different area. I was thinking of doing that if nothing else worked, and yeah, I forgot about the mean in the equation :slight_smile:

Thank you again, Dave!