Flood risk model

Example: A probabilistic risk model estimating mean annual disruption or expected annual damages to energy infrastructure from flooding.

Step-by-step guidance

1. Dataset-level metadata

Select the following values when describing your dataset:

  • Risk data type: loss (risk is expressed as expected losses)

  • Title: “Flood risk assessment for [infrastructure type/region]”

  • Description: Brief description of the risk model, infrastructure covered, and methodology

  • Publisher: Organization that developed the risk model

  • License: Appropriate license

2. Resources

Add resources for your risk model outputs:

  • Format: csv, geopackage, shapefile, or geotiff

  • Spatial resolution: Asset-level, grid-based, or administrative aggregation

  • Coordinate reference system: EPSG:4326 or appropriate projected CRS

3. Loss metadata

Under the Loss section:

Loss category

  • Category: economic (for monetary losses) or operational (for service disruption)

Risk metrics

Define the risk measures provided:

Metric 1 - Expected Annual Damage (EAD):

  • Loss type: risk

  • Dimension: structure or total

  • Unit: Currency code (e.g., USD)

  • Reference year: Year for currency valuation

  • Interpretation: Mean annual loss averaged across all flood return periods

Metric 2 - Annual Average Loss (AAL) (alternative term):

  • Loss type: risk

  • Dimension: As applicable

  • Unit: Currency code

Metric 3 - Service disruption:

  • Loss type: risk

  • Dimension: downtime

  • Unit: days or hours

  • Interpretation: Expected annual downtime due to flood events

Metric 4 - Affected population (optional):

  • Loss type: risk

  • Dimension: population

  • Unit: people

Return period losses (optional)

In addition to average annual metrics, you may include scenario losses:

  • Return periods: e.g., 10, 50, 100, 500 years

  • Loss values: Direct losses for each return period scenario

Hazard reference

Link to the flood hazard component:

  • Hazard type: flood

  • Processes: fluvial_flood, pluvial_flood, or coastal_flood

  • Return periods modeled: List of return periods used in risk calculation

  • Hazard dataset: Reference to the flood hazard maps used

Exposure reference

Link to the exposure component:

  • Exposure category: infrastructure (or buildings, population)

  • Asset types: Specify infrastructure types (e.g., power plants, substations, transmission lines)

  • Exposure dataset: Reference to the infrastructure inventory used

Vulnerability reference

Link to the vulnerability component:

  • Vulnerability functions: Reference to depth-damage curves used

  • Function IDs: List specific vulnerability functions applied

4. Spatial coverage

Define the geographic extent:

  • Scale: national, sub-national, or regional

  • Countries: Select applicable ISO 3166-1 alpha-3 country codes

  • Administrative regions: Specify if relevant

  • Infrastructure network: Describe coverage (e.g., entire national grid, specific utility service area)

  • Bounding box: Coordinates of modeled area

Example data structure

Your risk model outputs should include:

For asset-level risk:

  • Asset ID

  • Asset type/category

  • Location (coordinates)

  • Expected Annual Damage (USD)

  • Expected annual downtime (days)

  • Return period losses (10yr, 50yr, 100yr, 500yr)

  • Asset replacement value

  • Risk as % of asset value

Example CSV structure:

Asset_ID,Asset_Type,Longitude,Latitude,EAD_USD,AAD_Days,Loss_10yr_USD,Loss_100yr_USD,Replacement_Value_USD,Risk_Ratio
PS_001,Substation,100.523,13.756,125000,0.5,50000,450000,15000000,0.0083
PP_001,Power_Plant,100.612,13.821,450000,2.1,200000,1800000,250000000,0.0018
TL_045,Transmission_Line,100.445,13.698,35000,0.2,15000,120000,2000000,0.0175

For aggregated risk:

  • Geographic unit (admin area, grid cell)

  • Total EAD across all assets

  • Number of assets at risk

  • Population affected

  • Critical infrastructure count

Key considerations

Risk calculation methodology

  • Document the approach used to calculate risk:

    • Integration of loss-exceedance curve

    • Summation across return periods with probability weighting

    • Monte Carlo simulation

  • Specify assumptions:

    • Independence of flood events

    • Stationarity of hazard probabilities

    • Climate change considerations

Model components

Clearly document linkages:

  • Hazard: Which flood maps and return periods were used

  • Exposure: Which infrastructure inventory and attributes

  • Vulnerability: Which damage functions were applied

  • Dependencies: Cascade effects, network disruptions

Currency and valuation

  • Specify currency and reference year

  • Document valuation approach for infrastructure:

    • Replacement cost

    • Depreciated value

    • Service replacement value

  • Include indirect losses if modeled:

    • Business interruption

    • Wider economic impacts

    • Social costs

Uncertainty

  • Include uncertainty bounds if available:

    • Aleatory uncertainty (natural variability)

    • Epistemic uncertainty (model/parameter uncertainty)

  • Document sensitivity to key assumptions

  • Provide confidence intervals for risk estimates

Interpretation of risk metrics

  • Expected Annual Damage (EAD): Long-term average annual loss

    • Calculated by integrating the loss-exceedance curve

    • Represents the mean loss if same conditions persist over many years

  • Return period losses: Scenario-based losses for specific events

    • E.g., “100-year flood loss” is loss from 1% annual probability event

    • NOT the loss expected to occur once per 100 years

  • Risk ratio: EAD as percentage of asset value

    • Useful for prioritization and comparison across assets

Time horizon and discounting

  • Specify if risk is calculated for current conditions or future projections

  • Note any discount rate applied for multi-year analyses

  • Document climate change scenarios if included

Linking to other components

A complete risk assessment should reference:

  1. Hazard dataset: Flood maps used (with DOI or dataset ID)

  2. Exposure dataset: Infrastructure inventory (with version)

  3. Vulnerability dataset: Damage functions applied (with source)

  4. Validation: Historical losses used for calibration (if available)

This allows users to:

  • Understand the basis for risk estimates

  • Reproduce calculations

  • Update risk with new hazard scenarios or exposure data

  • Validate against observed losses