Leijnse and his fellow researchers argue that models with reduced complexity can provide detailed information about climate flood risks for large areas. This helps coastal managers to more effectively protect the coast from devastating hurricanes (such as Helene in 2024) or to improve warnings of immediate dangers from flood risks (such as the Spanish floods in 2024).
Devastating impact
Tropical storms and tropical cyclones have already affected hundreds of millions of people in coastal areas worldwide. Extreme weather can have a devastating impact on coastal communities, from flood damage to forced displacements and deaths.
Coastal managers need more accurate and actionable information about flood hazards so they can take measures to reduce disaster risk. This includes, for example, protective measures and warning systems.
Computing capacity
To do this, more detailed model simulations are needed on a large scale, and therefore more computing capacity. Leijnse developed new methods to efficiently scale up the modelling of coastal flood hazards, so that coastal communities have more accurate information at their disposal.
Leijnse: ‘First, we introduced 'Super-Fast INundation of CoastS’ (SFINCS): a new open-source model with reduced complexity, to more efficiently model composite flooding in coastal areas. In addition, we introduce a new efficient wave model to estimate the evolution of waves towards the coast. With this, flooding due to waves can also be considered efficiently.’
The researchers used various cases, such as that of hurricane Florence (2018), to demonstrate that these models can predict flooding on a spatial scale of 1000 kilometres.
Leijnse will defend his PhD thesis on 24 April.