Important national efforts are underway to better prepare for the elevated flood risk strongly linked to a nonstationary climate. The National Oceanic and Atmospheric Agency, for example, is developing a new Atlas 15 to be better aligned with current and future flood risk.

In addition, Stochastic Storm Transposition (SST) has emerged as a tool with the potential to help identify and assess flood risk under extreme conditions. The methodology uses high-resolution gridded radar data of recent storm events and leverages the assembled storm catalog to stochastically generate thousands of other plausible and realistic storms, ultimately creating a more detailed picture of how extreme storms could affect flood risk in a targeted area. To date, SST mainly has been used to project how a plausible set of storms may affect critical infrastructure such as dams and levees. As computational efficiencies improve—and the use of high-resolution meteorological data grows—the tool’s use is bound to become more widespread.

Developing a More-Complete Storm Picture

Simple statistical methodologies and minimal field measurements have long supported the engineering profession through decades of infrastructure design. Flood Frequency Analysis (FFA) and Intensity Duration Frequency (IDF) curves are based on observations recorded at rain and stream gauges. Values recorded in the field are fitted to parametric probability distributions to identify peak flows. This approach allows us to infer flows of variable return periods using minimal data inputs and basic statistical tools.

Hydrologists advocating for SST note that use of FFA and IDF can result in an overly simplistic picture of a storm’s impacts on a watershed. Conventional design approaches don’t track a storm’s orientation or its movement over variable terrain or other aspects of a storm’s “structure” as it affects a specific area. The SST methodology captures this variability by using real storm data to generate thousands of plausible storms, all of which provide key details on important watershed and meteorological interactions.

How SST Works

High-resolution gridded radar datasets for a meteorologically homogeneous region are the “powerhouse” behind the SST methodology. These data provide detail about how storms behave within the area of interest: storm spatial extent, precipitation patterns on the outskirts of the area vs. the center, storm dynamics through time and more. The larger and longer the record of storm data for a region, the greater the ability to identify thousands of distinct storm events, each with unique characteristics relating to intensity, orientation and movement through the watershed.

After a collection of storms based on gridded radar data is assembled, a computer program randomly samples subsets of the storm data to generate additional physically plausible ensembles of storms. These storms then can be mathematically moved or spatially “transposed” to different locations within the watershed to show precipitation trends through space and time. The resulting synthetically or computer-generated catalog of storms ultimately becomes a lengthier meteorological record that provides a visualization of how the most extreme and rarest storms may affect a target area.

Rainfall frequency curves then can be developed from the hyetographs computed through this process. In combination with a rainfall-runoff model, flood frequency curves can be developed as well as peak discharge estimates and locations of high flood-hazard zones likely to develop under extreme conditions (e.g., return periods of 1,000 years).

In a study involving the Arkansas River Watershed near Pueblo, Colo., the authors concluded that using SST expanded an otherwise data-poor stream-gauge record by tapping into a meteorologically robust storm catalog. When SST data were combined with a rainfall runoff model to generate flood frequency estimates, the results showed that storm size and watershed location significantly affected flood peaks—an effect the authors state would likely be missed using standard design-storm methods.

U.S. Application

The federal Bureau of Reclamation is using RainyDay, an open-source SST platform developed by University of Wisconsin–Madison Civil Engineering Professor Daniel Wright, to analyze dam and reservoir flood hazards in several basins. It’s increasingly viewed as an effective way to assess resilience and system performance when tested against the large ensemble of plausible storms SST generates. The U.S. Army Corps of Engineers also is experimenting with integrating SST into its HEC-HMS hydrology software to develop watershed-averaged precipitation frequency curves and identify locations susceptible to extreme flood flows.

SST still is experimental and requires substantial computational power, but it’s increasingly considered a solid planning tool by agencies charged with designing for extreme risk. Most importantly, the methodology depends on robust gridded storm data, ideally spanning decades. This data-centric aspect of SST means earlier risk assessments can be reexamined as new data become available. This isn’t exactly a real-time data input, but it’s close.

As this tool evolves and becomes more accessible to local agencies, it could offer an advantage as shifting precipitation trends and general climate uncertainty places a premium on agility and timely adaptation.