Metadata

name: debris-flow-risk
description: Identify potential debris-flow gullies, integrate terrain, material source, rainfall trigger, and downstream exposure to produce basin-level hazard screening and risk assessment.

Debris Flow Risk Screening

Identifies potential debris-flow gullies from DEM terrain analysis, integrates material source availability, rainfall triggering thresholds, and downstream exposure to produce basin-level hazard and risk screening.

Trigger

Use when the user wants to:

Identify potential debris-flow gullies from DEM data
Produce basin-level debris-flow hazard screening
Assess downstream exposure and risk from debris flows
Evaluate runout zones using conservative geometric diffusion
Generate risk maps integrating hazard, exposure, and vulnerability

CLI Usage

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

# With custom parameters
python scripts/debris_flow_risk.py \
  --rainfall-scenario 100yr \
  --runout-method geometric \
  --risk-schema three_class \
  --output-dir ./dfr-output

# With custom outlet points
python scripts/debris_flow_risk.py \
  --outlet-points ./outlets.geojson \
  --material-source sparse \
  --output-dir ./dfr-output

Parameters

Parameter	Default	Description
`--place`	None	Place name for AOI lookup
`--bbox`	None	Bounding box: "west,south,east,north"
`--aoi-file`	None	Path to AOI polygon (GeoJSON)
`--outlet-points`	None	Path to outlet points (GeoJSON)
`--rainfall-scenario`	50yr	Rainfall scenario: 20yr, 50yr, 100yr
`--material-source`	moderate	Material source: sparse, moderate, abundant
`--runout-method`	geometric	Runout method: geometric, ramms, flo2d
`--risk-schema`	three_class	Risk schema: three_class, four_class, five_class
`--infrastructure`	None	Path to infrastructure points (GeoJSON)
`--dem-resolution`	30	DEM resolution in meters (sensitivity parameter)
`--flow-threshold`	500	Flow accumulation threshold for channel initiation
`--output-dir`	./dfr-output	Output directory

Output

File	Description
`debris_flow_basins.geojson`	Identified debris-flow basins with attributes
`hazard_index.tif`	Hazard index raster (0-1)
`runout_zones.geojson`	Runout zone polygons
`exposure.csv`	Downstream exposure inventory
`screening_report.pdf`	Screening report (HTML-based)
`request.json`	Analysis request metadata
`dataset-manifest.json`	Dataset inventory
`output-manifest.json`	Output file inventory
`qa.json`	Quality assurance checks

Key Algorithms

D8 Flow Direction

Standard D8 encoding: 1=E, 2=SE, 4=S, 8=SW, 16=W, 32=NW, 64=N, 128=NE. Flow accumulation computed by recursive upslope contribution.

Basin Delineation

Watershed basins delineated from outlet points using flow direction. Basins filtered by slope, curvature, and flow accumulation criteria.

Hazard Index

Composite index integrating:

Terrain factor: slope, profile curvature, basin relief
Material source: loose sediment availability
Rainfall trigger: intensity-duration threshold exceedance

Runout Zone (Geometric)

Conservative geometric diffusion: runout distance = H / tan(α), where H is the elevation drop and α is the average fan angle (default 11°). RAMMS/FLO-2D interfaces reserved for future implementation.

Sensitivity Analysis

Outlet position, flow accumulation threshold, and DEM resolution are key sensitive parameters. Sensitivity analysis varies each parameter ±20% and reports hazard index change.

Risk Classification

Risk = Hazard × Exposure × Vulnerability Three-class: Low, Moderate, High

Exit Codes

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

Important Limitations

Output is screening-level, NOT a substitute for dynamic engineering models
Results are sensitive to outlet position, flow threshold, and DEM resolution
Runout uses conservative geometric diffusion; for engineering design use RAMMS/FLO-2D
Material source estimation is approximate without field validation
Rainfall thresholds are regional approximations

References

Takahashi, T. (2007). Debris Flow: Mechanics, Prediction and Countermeasures.
Hungr, O., et al. (2005). The Varnes classification of landslide types.
Horton, P., et al. (2013). Flow-R: a model for susceptibility mapping of debris flows.
Kang, S., & Lee, S. (2018). Debris flow susceptibility assessment using GIS and machine learning.

数据下载

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

python debris_flow_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)。

Geoskill: Debris Flow Risk Screening

安装技能