Vulnerability

Vulnerability#

Vulnerability determines how much harm a given exposure suffers once a hazard occurs. It is the third term in the pipeline’s IPCC-aligned framework, \(R = H \times E \times V\).

Definition

Vulnerability is “the conditions determined by physical, social, economic and environmental factors or processes which increase the susceptibility of an individual, a community, assets or systems to the impacts of hazards” (UNDRR, 2019). In this pipeline, vulnerability combines sensitivity (how exposed livelihoods are to a given hazard type) and adaptive-capacity deficit (how limited the means to cope or recover are).

In this pipeline#

V_drought – drought vulnerability
V_wet – excess-wetness vulnerability

Both are composite indices, normalized 0-1 on the same analysis grid as hazard and exposure, where higher values indicate higher vulnerability:

\[ V = 0.60 \cdot \text{sensitivity} + 0.40 \cdot \text{adaptive\_capacity\_deficit} \]

Formula: V_drought#

Weights are the live values in config/vulnerability_indicators.yaml – every raw indicator is first normalized 0-1 by robust 5th/95th-percentile scaling (same formula as in the “Formula” section of exposure.md), then direction is applied (invert for a beneficial raw indicator, e.g. higher irrigation

\[ \text{sensitivity}_{drought} = 0.5 \cdot S_{poverty} + 0.5 \cdot S_{aridity} \]

\[ \text{adaptive\_capacity\_deficit}_{drought} = 0.5 \cdot S_{irrigation\_deficit} + 0.5 \cdot S_{no\_electricity\_access} \]

\[ V_{drought} = 0.60 \cdot \text{sensitivity}_{drought} + 0.40 \cdot \text{adaptive\_capacity\_deficit}_{drought} \]

S_no_electricity_access is a derived indicator, not a direct layer: gdessa_no_access / population, clipped to [0, 1] (see src/hydroclim_risk/vulnerability/indicators.py).

Formula: V_wet#

\[ \text{sensitivity}_{wet} = 0.20 \cdot S_{poverty} + 0.20 \cdot S_{elevation} + 0.20 \cdot S_{slope} + 0.20 \cdot S_{soil\_clay} + 0.20 \cdot S_{river\_distance} \]

\[ \text{adaptive\_capacity\_deficit}_{wet} = 0.5 \cdot S_{healthsites\_deficit} + 0.5 \cdot S_{no\_electricity\_access} \]

\[ V_{wet} = 0.60 \cdot \text{sensitivity}_{wet} + 0.40 \cdot \text{adaptive\_capacity\_deficit}_{wet} \]

The five sensitivity_wet indicators are weighted equally (0.20 each) – elevation captures regional low-lying-ness, slope captures local flatness/drainage, soil clay captures infiltration capacity, and river distance captures direct flood exposure, so they’re treated as complementary rather than redundant (no methodology-specified sub-weighting exists yet).

Vulnerability sub-indicators and data sources#

Sub-indicator	Feeds	Source	License
Relative wealth index	Sensitivity (both)	Meta Data for Good	CC-BY 4.0
Irrigated area percent	Adaptive capacity (drought)	FAO GMIA v5	CC-BY 4.0
Aridity index	Sensitivity (drought)	CGIAR-CSI / Global Aridity Index v3	CC-BY 4.0
Population without electricity access	Adaptive capacity (both)	IIASA/GDESSA	CC-BY 4.0
Elevation and slope	Sensitivity (wet)	NOAA ETOPO 2022	Public domain
Topsoil clay content	Sensitivity (wet, drainage)	ISRIC SoilGrids 2.0	CC-BY 4.0
Distance to rivers/water bodies	Sensitivity (wet)	OpenStreetMap (via Geofabrik)	ODbL

Full citations, acquisition modules, and output file names are in data_provenance.md.

Layer statistics#

Composite indices, computed on the analysis grid (post country-mask):

Layer	Valid cells	Min	Mean	Max
V_drought (0-1)	1,489 / 2,880	0.09	0.58	0.97
V_wet (0-1)	1,485 / 2,880	0.28	0.56	0.81

Raw sub-indicators (domain grid, before the final country-boundary mask):

Sub-indicator	Min	Mean	Max
Relative wealth index	-1.16	-0.46	0.17
Irrigated area (%)	0.0	0.7	88.0
Aridity index	0.00	0.34	1.47
Population w/o electricity access	766	58,451	2,599,120
Slope (degrees)	0.0	2.1	20.4
Topsoil clay content (%)	12.5	32.0	62.8
Distance to river/water body (km)	0.0	74.5	636.6

Representative figures#

V_drought	V_wet

Composite vulnerability, normalized 0-1, Ethiopia-clipped.

Inspect the full layers#

Two notebooks carry the complete, machine-readable detail behind this page. Both are listed in the left sidebar under the Exposure page (jupyter-book’s table of contents does not allow the same notebook to be nested under two different pages).

Exposure and vulnerability GeoTIFF notebook

inspect_exposure_geotiffs.html – V_drought and V_wet maps, component interpretation notes, summary statistics, and boundary-clipped previews.

Raw source inspection notebook

inspect_exposure_vulnerability_raw_sources.html – native inputs (aridity, irrigation, electrification access, waterways and water bodies, relative wealth index) prior to resampling.