Research line 2: Probabilistic, scale-aggregated modelling of CC driven coastal hazards

 

It is now known that simplistic but easy-to-use techniques such as the Bruun rule are unlikely to produce robust and accurate results. Highly aggregated models require large data sets to ‘train’ the models, which are generally unavailable, while time and space integration of micro-scale processes via process based models has also proved to be an insurmountable challenge where sufficiently accurate predictions of long term coastal change are concerned.  What coastal zone managers and planners worldwide need are easy-to-use models that are capable of providing robust estimates of the physical impacts of CC on coasts at time scales of 50-100 yrs. A particular need is to develop simplified (and therefore fast) models which lend themselves to multiple simulations (for example, within a Montecarlo simulation) and thereby facilitate probabilistic estimates of CC driven coastal change as required by contemporary risk based coastal zone management/planning frameworks.

 

An effective approach to develop physics based, yet simple and fast numerical models is via scale-aggregation. This reduced complexity approach adopts simplified descriptions of fundamental system physics and delivers estimates of system response to forcing. It is only very recently that some initial steps have been taken to develop such numerical models. This research line will focus on developing a suite of simplified physics, scale-aggregated numerical models (i.e. reduced complexity models) to derive probabilistic estimates of at least the 1st order coastal CC impacts, using the fundamental scientific process knowledge gained from Research Line #1. The probabilistic coastal hazard estimates obtained via these models will then facilitate the development of innovative coastal risk assessment methods.