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Flood Risk Data LULC In Urban Areas

Flood Risk Data LULC In Urban Areas This script prepares core geospatial layers for analyzing flood-prone areas in Bangkok. It exports elevation, land cover (water and built-up areas), and October 2023 rainfall data to local GeoTIFF files using Google Earth Engine and geemap. The outputs are used for flood vulnerability analysis, especially in low-lying, urbanized zones.

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Output File

/data/bangkok_flood_native

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Requirements

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Files Created

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High-Level Workflow Steps

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1. Setup and Define AOI

Define Bangkok’s location and buffer by 10 km to create an Area of Interest (AOI).

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2. Export SRTM Elevation

  • Use the USGS/SRTMGL1_003 dataset
  • Export as dem_srtm.tif at 30 m resolution

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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.tif at 10 m resolution

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4. Export Rainfall

  • Use CHIRPS Daily rainfall dataset
  • Sum rainfall for October 2023
  • Export as rainfall_oct2023_chirps.tif at ~5.5 km resolution

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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

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Output: Flood-Prone Map

You get a matplotlib map showing flood-prone areas based on both elevation and landcover:

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Legend Explained

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