The Data Behind Flood Factor

Flood Factor uses the First Street Foundation Flood Model to find flood risks across the United States.

The First Street Foundation Flood Model is a nationwide, probabilistic flood model that shows any location’s risk of flooding from rain, rivers, tides, and storm surge. It builds off of decades of peer-reviewed research and forecasts how flood risks will change over time due to changes in the environment.

* Risk is calculated as inundation of 1 cm or more to the building in the 500 return period (0.2% annual risk). Basemap imagery © CARTO.

Providing full flood risk analysis.

To provide a 360˚ view of flood risk, the model has recreated many major U.S. floods and generated full coverage, high-resolution flood maps projecting current and future risks for more than 142 million properties across the contiguous 48 states. It additionally provides Federal Emergency Management Agency (FEMA) flood zone information for properties, as well as aggregated analyses of neighborhoods, zip codes, cities, counties, and states.

2015

This year

In 30 years

Toledo, Ohio: Past flood in 2015, current 1 in 500 year flood, and 1 in 500 year flood in 30 years. Basemap imagery © Mapbox

Analyzing all major flood types.

The First Street Foundation Flood Model considers a location’s risk of flooding from high intensity rainfall, overflowing rivers and streams, high tides, and coastal storm surge. Because these different flood types are often interconnected, they are first analyzed independently, then “coupled” together to create one, unified flood risk model.

Rain

High intensity rainfall causes flooding when an area’s sewage system and draining canals lack the necessary capacity to drain away the amount of rain that falls.

Urban areas are particularly susceptible because there is little open soil that can store water.

Calculating and mapping flood probabilities.

The First Street Foundation Flood Model is a probabilistic flood model, which means it considers uncertainty and outputs a distribution of likelihoods. It first asks the question: “what is the likelihood of a flood occurring within a given year?” Based on a location’s history and geographic information (such as elevation, climate, proximity to water, and adaptation measures), the model creates a range of probabilities, known as “return periods.”

The model then analyzes select probabilities (0.2%, 1%, 10%, 20%, 50%) to create “hazard layers,” which show where and how deep flooding could occur for each probability. This allows First Street Foundation to not only calculate, but actually map flood risks for different probabilities within a given year.

50%

Flood likelihood Today

A 50% (1/2) annual chance of flooding is about as likely as a flipped coin landing on tails.

0
0.5
1
2
3+ft

Depth of flooding (feet)

Basemap imagery © Mapbox

Determining future flood risks.

The inclusion of environmental changes that impact flood risks, such as sea level rise and precipitation patterns, is an essential trademark of the First Street Flood Model. Using 1980-2010 as a baseline period, the model analyzes multiple environmental possibilities under the RCP 4.5 carbon emissions scenario with high and low uncertainty bounds. The resulting high, median, and low environmental scenarios are then used as inputs when calculating current and future flood risks to show how flood risks will change in fifteen and in thirty years.

IPCC Representative Concentration Pathways

Simplifying flood risks.

Because the First Street Foundation Flood Model can calculate a flood depth for any probability, it can also determine the likelihood of a flood reaching a minimum depth in a given year, known as an annual flood likelihood. A property’s annual flood likelihood for a specific depth in 15 or 30 years may differ from its annual likelihood this year because of changes in the environment.

Relationship between flood depth and likelihood

Flood risks do not occur on an annual basis, but rather accumulate over time. To simplify this, the model also calculates a cumulative flood likelihood, which shows the likelihood of flooding to a certain depth at least once over 15 or 30 years. For instance, if a home has a 1% annual flood likelihood (also know as a 100-year flood risk), that home has a 26% chance of flooding at least once over 30 years.

Cumulative Probabilities

Annual
Probability
Over
5 years
Over
15 years
Over
30 years
50% (1/2)
97%
100%
100%
20% (1/5)
67%
97%
100%
10% (1/10)
41%
79%
96%
1% (1/100)
5%
14%
26%
0.2% (1/500)
1%
3%
6%

Scoring system for properties.

To simplify flood risks even further, the First Street Foundation Flood Model also calculates a property’s Flood Factor. Flood Factor scores increase as the 30-year cumulative flood likelihood increases, or as the projected depth of flooding increases. Properties with higher Flood Factors are either more likely to flood, are more likely to flood, are more likely to experience high floods, or both.

Flood Factor Matrix

 

While it is still possible for properties with a Flood Factor of 1 (minimal) to flood, they are not included in the Flood Factor matrix. These properties have a less than 0.2% chance of flood water reaching the building in every analyzed year.

Calculating yearly financial impact.

Average annual loss data provides a powerful way to understand flood risk by evaluating it in terms of its potential dollar cost to property owners. The annual flood damage cost estimates refer to how much, on average, a property-owner could expect to pay in repair costs from flood damage to their building in a given year. It is calculated based on a property’s flood risks, the likelihoods and depths of flooding projected for a building and building characteristics, such as property type, elevation, materials, building value. These estimates do not include costs to replace damaged contents, and do not consider inflation. Because the risk of loss changes as environmental conditions change, these costs generally increases over time alongside flood risk to a property.

To assess average annual loss, First Street Foundation applied its comprehensive, property specific flood model to historical data from the United States Army Corps of Engineers, which tracks flooding to properties at various depths and combines this data with records of FEMA insurance claims.

 

Sample of contributing factors to higher annual flood damage estimates

More expensive buildings cost more to repair because of the extent of damage and cost of materials, so total costs over time will be higher.

 

Each assessment is based directly on a property’s probability of flooding, so it is relative to each property’s unique risk. The damage costs are also relative to a property’s value; so homes with higher risks and/ or higher values will have higher average annual loss estimates. Other variables, such as ground floor elevation and the presence of a basement can also impact the depth to damage relationship, and can be adjusted on the property-level to provide more precise estimates.

Ensuring scientific accuracy.

The creation of the First Street Foundation Flood Model required an unprecedented partnership of more than 80 world-renowned scientists, technologists and analysts. The data the model produces undergoes multiple reviews and must pass comprehensive check-points before being made publicly available.

  • Where possible, data has been validated against historic flood reports and government flood claims.
  • In developing its flood model, First Street Foundation created the first national adaptation database, including more than 40 different adaptation types, which is used to both inform and validate flood projections.
  • All methods used by the First Street Foundation Flood Model have undergone an expert academic panel review and have been submitted to scientific peer-review journals.

Continuously improving over time.

First Street Foundation has made its flood model’s full technical methodology available to the public because it supports scientific collaboration and data transparency. The First Street Foundation Flood Model will continue to incorporate feedback and expand its model over time, including an annual data update.

Since its release in June 2020, Flood Factor has continued to improve its data quality and site experience. As new data and model improvements become available, some properties may see changes in their flood risks.

 

September 2020 Release Highlights:

  • Millions of new properties analyzed and added
  • More coastal cities and counties with bi-annual flood projections (50% annual chance), and cleaner coastal flood layers
  • Local Flood Factor score maps are now viewable on property pages

Why are flood risk increasing?

A changing environment means higher seas, new weather patterns, and stronger storms.

What can be done to stop flooding?

Although flood risk can never be completely eliminated, there are a wide range of flood protection measures that reduce risk.

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