Geoskill: Coastal Flood Risk

Coastal flood risk assessment using static bathtub inundation with ocean connectivity. Simulates sea level rise and storm surge scenarios, assesses population/building/infrastructure exposure, and identifies adaptation priority zones.

Coastal Flood Risk

Static bathtub inundation model for coastal flood risk assessment. Simulates flooding under sea level rise and storm surge scenarios, assesses exposure of population, buildings, roads, and critical facilities, and identifies adaptation priority zones.

Trigger

Use when the user wants to:

• Simulate coastal flooding under sea level rise or storm surge scenarios
• Assess population and infrastructure exposure to coastal flooding
• Compare multiple flood scenarios (e.g., 0.5m/1m/2m SLR)
• Identify adaptation priority zones in coastal areas
• Generate flood risk reports for coastal planning

CLI Usage

bash

# Synthetic demo mode (no input files needed)
python scripts/coastal_flood_risk.py --output-dir ./cfr-output

# With custom water levels
python scripts/coastal_flood_risk.py --water-levels "0.5,1.0,2.0" --output-dir ./cfr-output

# With coastal defenses
python scripts/coastal_flood_risk.py --defense-height 2.0 --output-dir ./cfr-output

# Without ocean connectivity (pure bathtub)
python scripts/coastal_flood_risk.py --connectivity false --output-dir ./cfr-output

Parameters

Parameter	Default	Description
--dem	None	Path to DEM GeoTIFF (synthetic if omitted)
--water-levels	0.5,1.0,2.0	Comma-separated water levels in meters
--vertical-datum	MSL	Target vertical datum (MSL/NAVD88/WGS84/unknown)
--connectivity	true	Apply ocean connectivity filter
--defense-height	0.0	Coastal defense height in meters
--defenses	None	Path to defense vector/raster file
--scenarios-config	None	Path to flood scenarios JSON
--output-dir	./cfr-output	Output directory

Output

File	Description
report.html	Human-readable flood risk report
priority_zones.geojson	Adaptation priority zones
exposure_by_scenario.csv	Per-scenario exposure statistics
depth_proxy.npy	Depth proxy array (synthetic mode)
request.json	Analysis request metadata
dataset-manifest.json	Dataset inventory
output-manifest.json	Output file inventory
qa.json	Quality assurance checks

Key Algorithms

Bathtub Inundation with Ocean Connectivity

Cells below the water level that are connected to the ocean (via flood-fill from edge cells) are marked as flooded. This eliminates inland depressions that are not actually connected to the sea — a common false positive in simple bathtub models.

Depth Proxy

Depth = water_level - elevation for flooded cells. This is a static proxy, NOT true hydrodynamic depth. It overestimates depth in areas with drainage or wave protection.

Exposure Assessment

For each asset category (population, buildings, roads, critical facilities), computes total exposed value, exposed fraction, and depth-weighted exposure.

Priority Zones

Cells flooded in >= 2 scenarios AND with depth >= threshold are identified as adaptation priority zones. Zones are ranked by priority score (depth x n_scenarios).

Exit Codes

Code	Meaning
0	Success
2	Argument error
3	Dependency missing
6	Data validation failure
7	Processing failure

Important Limitations

• Static bathtub, NOT hydrodynamic: This model does not simulate wave propagation, drainage, or tidal dynamics. Do not present results as "storm surge simulation" — it is a static inundation estimate.
• Vertical datum critical: DEM and water levels MUST share the same vertical datum. If the datum is unknown, results are RELATIVE only.
• No defense data = unprotected scenario: Without defense data, results represent an unprotected coastline. This is a worst-case estimate.
• Connectivity eliminates false positives: Inland depressions below sea level are correctly excluded when connectivity is enabled.

References

• IPCC AR6 Sea Level Rise projections
• USACE Coastal flood risk assessment guidelines
• Eurocode flood hazard mapping standards

数据下载

本 skill 可自动从 Microsoft Planetary Computer 下载数据 (无需 API key):

bash

python coastal_flood_risk.py --bbox 116,39,117,40 --date-range 2024-06-01,2024-06-30 --output-dir <tmp>

• --bbox W,S,E,N: WGS-84 边界框 (西, 南, 东, 北)
• --date-range START,END: 日期范围 (YYYY-MM-DD,YYYY-MM-DD)
• --aoi-file <path.geojson>: 替代 --bbox 的 GeoJSON 多边形
• --cache-dir <path>: 缓存目录 (默认 ~/.geoskill_cache)

当用户只给 --bbox + --date-range (没有 --dem) 时，skill 自动下载数据。 当用户给 --dem 时，走原文件路径 (向后兼容)。