Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence 15 of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal 16 management. This study aims to develop and to validate two different models to predict the 17 sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for plan-18 etary-scale environmental data analysis. The first model is a Bathtub Model based on the uncer-19 tainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and 20 Modeling System software. The validation process performed in the Rio Grande do Sul coastal 21 plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule for-22 mula implemented in GEE and is capable to determine the coastline retreat of a profile through the 23 creation of a simple vector line from topo-bathymetric data. The model shows a very high correla-24 tion (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE 25 platform seems to be an important tool for coastal management. The models developed have been 26 openly shared, enabling the continuous improvement of the code by the scientific community.
Ionospheric dynamics and drivers obtained from a physics-based data assimilation model
