Sea Level Rise Simulation Map

The Assignment

Inspired by the assignment on land cover classification methods and the Google Earth Engine carbon sequestration map, I decided to create a sea level rise simulation coupled with a land cover type map. Coupling them allows decision makers to understand which areas will be most affected by sea level rise, what land cover type they represents and in what time frame they will be subject to becoming "drown areas". This app is focused on the island of Fiji as it is part of the group of Small Island Devloping States most vulnerable to sea level rise.

Data Collection

Land cover: ESA World Land COver 2020
Elevation: NASADEM Digital Elevation 30m

The Process

1. Defined the area of interest (AOI) as Fiji using a feature collection.
2. Added ESA WorldCover 2020 land cover data, clipped it to the AOI, and visualized it with a specified color palette.
3. Added NASADEM elevation data, clipped it to the AOI, and created hillshade and DEM visualizations.
4. Developed a sea level rise simulation function based on sea level rise per year and target year.
5. Implemented UI sliders for dynamic control of sea level rise and target year.
6. Created and added legends for land cover, elevation, and simulated sea level rise to the map.
7. Ran the GEE script to visualize land cover, elevation, and sea level rise simulation for the AOI.

What can be seen

According to the simulation, north-west areas (around Ba) and south-east areas (around Suva) of Viti Levu, Fiji's largest and main island, are most susceptible to become drown areas by 2100. In the north-west, these are shown to be mostly permanent water bodies, grasslands and mangrove forests. In the south-east, they are shown to be mostly mangroves and moss and lichen environments. It is important to note that this remains a hypothetical scenario, which includes many assumptions. For isnatnce, the simulation assumes that sea level rise increases linearly, and that the land cover will kept the same all the way until 2100, an unlikely event given rates urbanization and deforestation. The binary classfication of "drowned areas" versus "non-drowned areas" does not account for local variations in topography and the presence of protective infrastructure. Nonetheless, it is valuable information to determine which areas to prioritize in climate and sea level rise adaptation efforts.

Feedback

From students and instructor.