Risk Model Offers Flood Damage Forecasting


A Johns Hopkins University expert has reportedly developed a new natural disaster risk modeling method to more accurately estimate damage expected from riverbank flooding.

According to the university, the new process provides users with step-by-step instructions, as well as measures and assigns numerical values to the level of uncertainty in individual flood damage forecasts.

“The bottom line is that accurate predictions are crucial to the safety and well-being of people and property. If a government acts based on inaccurate information, its preparation can be off by orders of magnitude, with very serious results,” said Gonzalo Pita, an associate research scientist in the Whiting School of Engineering's Department of Civil and Systems Engineering, as well as an instructor in the Johns Hopkins Engineering for Professionals' civil engineering program.

In the new study, Pita reportedly first investigated the accuracy of using expert opinion alone to estimate and predict flood damage. He surveyed multiple authorities and simulated thousands of expert surveys in numerous combinations to analyze how the composition of the expert team influences prediction accuracy.

“What I found was a variability in accuracy of between 10% and 46% among experts, which is a wide range,” Pita said. “I also learned that accuracy was enhanced by adding additional expert voices, rather than simply adding more questions to the survey.”

Afterwards, Pita addressed the issue of “damage functions,” or a fundamental component of natural disaster risk simulations.

A function refers to the mathematical relationship between two variables—in this case, a depth-damage function describes the correlation between the depth of flood waters and the level of damage it causes.

For example, “1 foot of flood water in a home causes $10,000 in damage.” According to Pita, creating these functions accurately is out of reach for many governments.

"It is a very expensive process in terms of money and time, and poor countries—and even wealthy ones—sometimes may not have the expertise, time, or data to develop them within an acceptable timeframe,” he explained.

“So with this method, these functions can be built inexpensively but with a useful level of accuracy that governments can use provisionally until they get better data that will enable them to generate more accurate functions."

The new approach reportedly works by helping quantify the uncertainty in experts’ predictions by assigning weights to each expert, resulting in a more detailed analysis of the uncertainties involved.

According to Johns Hopkins, the result is a method that Pita expects to be “very useful” for flood modelers and influencing preparedness policy.

“These types of insights could inform policy directly and indirectly, from enabling smarter zoning laws and budgeting for asset maintenance to designing disaster insurance programs. All of this is to say that better flood damage data and predictions have the potential to have far-reaching benefits,” he said.

The study appears in The International Journal of Disaster Risk Reduction. It builds upon work that previously appeared in The Journal of Hydrology.

Other Flooding Studies

At the beginning of 2021, researchers from Stanford University found that the United States has spent nearly $199 billion in flood damages over the last three decades.

However, researchers went a step further than just comprising a spending report and further indicated that the flooding experienced from 1988 to 2017 as a result of intensifying precipitation—consistent with predictions of global warming—was responsible for one-third ($75 billion) of the total financial costs.

The research was published in the journal Proceedings of the National Academy of Sciences and was reported to have helped resolve a long-standing debate about the role of climate change in the rising costs of flooding and provided new insight into the financial costs of global warming overall.

Several states, including FloridaTexas and Massachusetts, have outlined spending plans and funding proposals for flooding and rising sea levels. At the end of that year, New York officials also unveiled their design for its Financial District-Seaport Climate Resilience Plan, part of the larger Lower Manhattan Coastal Resiliency project.

In October 2021, nonprofit flood research and communications group, First Street Foundation, reported that one in four units of critical infrastructure in the United States—such as police stations, airports and hospitals—are at risk of being rendered inoperable due to flooding.

In addition to critical infrastructure, the report highlighted at-risk residential properties, roads, commercial properties and social infrastructure and how that risk is expected to worsen over the next 30 years with respect to climate change.

According to FSF, the risks are quantified by an area’s unique level of flooding for each infrastructure type relative to operational thresholds, as established by the federal government and other authoritative bodies.

Over the next 30 years, the report’s analysis found that risk is expected to increase by the following:

  • Residential properties – 10%;
  • Social infrastructure properties (government buildings, historic buildings, houses of worship, museums and schools) – 9%;
  • Commercial properties – 7%;
  • Critical infrastructure properties (airports, fire stations, hospitals, police stations, ports, power stations, superfund/hazardous waste sites, water outfalls and wastewater treatment facilities) – 6%; and
  • Roads – 3%.

While these risks are concentrated along coastal areas in the Southeastern U.S., they are also present in the Appalachian Mountain region. Among counties, Washington County, North Carolina has the most significant county level increase in flood risk; with a 100% increase in critical infrastructure flooding, a 50.8% increase in the flooding of residential properties, a 51.7% increase in the risk of flooding of commercial properties and a 32.3% increase in the flooding of roads over up to the year 2051.

In terms of community risk, the highest concentrations were reportedly witnessed in Louisiana, Florida, Kentucky and West Virginia.


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