High spatial resolution data obtained by GNSS and RPAS to assess islets flood-prone scenarios for 2100

Abstract

The flood-prone scenarios assessment contributes to detecting natural and climate change trends and it is a crucial component of integrated coastal zone management. However, the data acquisition with a high spatial and temporal resolution for a local scale is still a challenge considering that sea-level rise projections are usually represented by global scales. This contribution uses locally acquired topographic data for a flood-prone simulation (2100), presents a flood depth-damage function, and points out several obstacles for sea-level rise simulations. The input data is based on GNSS and RPAS surveys. The results showed multimedia videos and maps containing the optimistic, intermediate, and pessimistic scenarios for 2100. In the pessimistic scenario (0.80 m elevation), 45% of the vegetation and 67% of the islet would flood. The results showed the importance and barriers for flood-prone simulations. It is still necessary to advance in developing new methods able to combine multiple parameters, particularly for local and regional scales highlighting high spatial data-set to properly represent local impacts.