The effects of floods are increasing as a result of global warming. Coastal, marine and watershed flooding, cause damage to people, the environment and economic activity. The damage to the economy is primarily affected by urban areas close to the coast and inland waterways, where the technological and underground infrastructure of buildings contribute to the damage of structures. The floods result in damages of more than EUR 5 million annually in Finland, for example for property owned by municipalities.
Extreme weather events are intensifying as a result of climate change, as well as rising sea levels, will pose increasing risks to flood-sensitive areas. Consequently, damages and other costs from flooding will also increase. It is therefore important to consider adaptation measures when mapping flood risk areas in order to minimise the damage and costs of flooding. As a solution to this, we used a GIS-based solution that allows us to combine flood risk areas and flooding costs for each sector through an analysis of spatial datasets.
It is important to highlight how the combination of flood data and statistical data provides added value, e.g., evaluating industry specific impacts at an aggregated level. The focus of the presentation is the importance of accurate spatial data and statistical data in examining the impacts on different regional levels.
In the KUITTI-project, we used SYKE's Flood dataset, which describes, among other things, the population of the flood hazard area and the floor area of buildings at 250x250meter rasters for different types of floods corresponding to the current climate.
Rasters have been calculated by overlapping analysis of flood hazard zones and building and apartment registry ranges separately for each flood probability (recurrence time). The location of the rasters are equivalent to that of Statistics Finland's regional division of municipalities.
We attached the data from Statistics Finland's raster database to SYKE's flood risk raster dataset. In this way, we were able to examine, for example, which industry the potential impact is being applied to.
This highlights the importance of spatio-temporally high-resolution datasets, which form a base for further regional and national analyses.
How to cite: Filla, S., Rautio, T., Sane, M., and Veijalainen, N.: Added value of spatially high-resolution flood and statistical data: Starting from Assessment of the cost of inaction regarding climate change, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-648, https://doi.org/10.5194/ems2022-648, 2022.
