Flood Risk Data LULC In Urban Areas
Output File
/data/bangkok_flood_native
Requirements
| Requirement | Details |
|---|---|
| Software | Python 3.x, Earth Engine Python API, geemap |
| Earth Engine | Enabled Google Earth Engine account (https://signup.earthengine.google.com) |
| Python Packages | geemap, earthengine-api, os, ee |
| Internet | Required for Earth Engine data download |
Files Created
| Filename | Description | Resolution |
|---|---|---|
| dem_srtm.tif | Elevation data from SRTM (clipped to AOI) | 30 m |
| dw_water_built_mask.tif | Masked Dynamic World image (1=Water, 2=Built) | 10 m |
| rainfall_oct2023_chirps.tif | CHIRPS total rainfall for Oct 2023 | ~5.5 km |
High-Level Workflow Steps
1. Setup and Define AOI
Define Bangkok’s location and buffer by 10 km to create an Area of Interest (AOI).2. Export SRTM Elevation
- Use the USGS/SRTMGL1_003 dataset
- Export as
dem_srtm.tifat 30 m resolution
3. Export Landcover (Water & Built)
- Use the Google Dynamic World dataset for 2023
- Use
.mode()to get the most frequent class - Remap values: 0 → Water (1), 6 → Built (2), others → 0
- Export as
dw_water_built_mask.tifat 10 m resolution
4. Export Rainfall
- Use CHIRPS Daily rainfall dataset
- Sum rainfall for October 2023
- Export as
rainfall_oct2023_chirps.tifat ~5.5 km resolution
Flood Risk Classification
This script generates a flood risk classification map of Bangkok using:- SRTM DEM (Elevation at 30m) to identify low-lying areas (< 5 meters elevation)
- Dynamic World Landcover (10m) to locate built-up and water regions
Output: Flood-Prone Map
You get a matplotlib map showing flood-prone areas based on both elevation and landcover:Legend Explained
| Color | Label | Meaning |
|---|---|---|
| 🔵 Blue | Low-lying Water | Areas classified as water in DW and below 5 meters elevation |
| 🔴 Red | Low-lying Built-up | Areas classified as built-up and below 5 meters elevation |
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