Understanding Disaster Risk Scores

Why Risk Scores Exist

Raw disaster data, counts of events, dollar damage, fatalities, tells you what has happened. Risk scores attempt to tell you what is likely to happen in the future and how badly it will hurt. This distinction matters enormously for decision-making: a county that has had few historical disasters but sits on an active fault line with a growing, vulnerable population may face higher future risk than a county with a long but manageable flood history.

FEMA, state emergency management agencies, insurers, and urban planners all use risk scores to allocate limited resources. Without some form of composite risk assessment, every community would argue that its hazards deserve the most attention. Risk scoring provides a common framework, imperfect, but standardized, for making those comparisons.

PlainHazard's state pages and county pages show the historical disaster record that underlies many of these risk models. Use them alongside risk scores to see whether the model matches the observed reality.

Key Metric: Expected Annual Loss (EAL)

What it tells you: Expected Annual Loss is the estimated average dollar value of damage a community can expect each year from natural hazards. It is calculated by multiplying three factors: the annualized frequency of hazard events, the exposure (people and property in harm's way), and the historical loss ratio (what percentage of exposed assets are typically damaged). EAL can be broken into building losses, population-equivalent losses, and agricultural losses.

What it doesn't tell you: EAL is an average, it smooths out the difference between years with no disasters and years with catastrophic ones. A county with a $10 million EAL might experience $0 in damage for 9 years and then $100 million in the tenth year. EAL also does not capture indirect economic losses like business interruption, population displacement, or long-term health impacts. It focuses on direct physical damage to structures and crops.

How to use it: Compare EAL across counties or states to understand relative risk exposure. A county with $50 million in annual expected losses from hurricanes has a fundamentally different risk profile than one with $500,000. Use EAL to contextualize the historical disaster counts on PlainHazard, high declaration counts with low EAL suggest frequent but low-damage events, while low declaration counts with high EAL suggest infrequent but catastrophic potential.

The Three Pillars of Composite Risk

Modern disaster risk assessment goes beyond "how likely is a hazard" to ask "how bad will it be and can the community recover?" FEMA's National Risk Index exemplifies this approach with three pillars:

Practical Framework: Reading a Composite Risk Score

When you encounter a disaster risk score for a county or region, use this framework to interpret it:

1. Understand the scale. Is the score relative (percentile ranking among all US counties) or absolute (dollar amount of expected loss)? A county at the 85th percentile for risk is higher risk than 85% of US counties, but the absolute dollar exposure depends on the county's size and value of assets.
2. Break it into components. A high composite score could mean high hazard probability with average vulnerability, or average hazard probability with extremely high social vulnerability. The prescription is different: the first needs mitigation infrastructure, the second needs social support programs. Always look at component scores, not just the composite.
3. Cross-reference with history. Check the county's actual disaster declarations and storm events on PlainHazard. If the model says high risk but history shows few events, the risk may be driven by low-probability, high-impact scenarios (like earthquakes in seismically active but historically quiet zones). If history shows frequent events but the model says moderate risk, the events may be low-damage.
4. Identify the dominant hazard. Most counties' risk is driven by one or two hazard types. A coastal Texas county's risk is dominated by hurricanes and flooding. A central Oklahoma county's risk is dominated by tornadoes and severe storms. Knowing the dominant hazard helps you focus preparedness efforts on what matters most.

Limitations of Risk Scores

Risk scores are models, simplified representations of complex systems. Key limitations to keep in mind:

• Backward-looking models: Most risk scores are calibrated on historical data. Climate change is altering hazard patterns faster than models update, wildfire risk in the Western US, intensifying hurricanes in the Gulf, and increasing inland flood risk from atmospheric rivers may all be underestimated.
• Data gaps: Some hazard types have sparse historical records, especially in areas that have only recently been populated. Wildfire risk in the wildland-urban interface is growing rapidly as development expands into fire-prone areas.
• Aggregation effects: County-level scores average risk across the entire county. A county might have extreme flood risk in river valleys and near-zero flood risk on hilltops. Census-tract-level data (available in the NRI) provides finer resolution.
• Compound events: Most models assess hazards independently. A hurricane that causes wind damage, storm surge, and inland flooding is one event with multiple hazard components, but models may score each separately without capturing the compounding effect.

Frequently Asked Questions