Study shows need to revise existing methods for estimating flood risk

photo of flood waters splashing against a river bank

This story was adapted from a news release by the Desert Research Institute.

The method used to calculate flood frequencies is due for an update, according to a new study by scientists from Colorado State University, the Desert Research Institute, and the University of Wisconsin-Madison.

Flood frequency analysis is a technique used to estimate flood risk, providing statistics such as the “100-year flood” or “500-year flood” that are critical to infrastructure design, dam safety analysis, and flood mapping in flood-prone areas.

Floods, even in a single watershed, are known to be caused by a variety of sources, including rainfall, snowmelt, or rain-on-snow events in which rain falls on existing snowpack. However, flood frequencies have traditionally been estimated under the assumption these flood “drivers,” or root causes, are unimportant.

In a new open-access paper in Geophysical Research Letters, a team led by Guo Yu of DRI examined the most common drivers (rainfall, snowmelt, and rain-on-snow events) of historic floods for 308 watersheds in the Western U.S., and investigated the impact of different flood types on the resulting flood frequencies.

Their findings showed that most watersheds – 64 percent – frequently experienced two or three flood types throughout the study period, and that rainfall-driven floods, including rain-on-snow, tended to be substantially larger than snowmelt floods across watershed sizes.

Further analysis showed that by neglecting the unique roles of each flood type, conventional methods for generating flood frequency estimates tended to result in under-estimation of flood frequency at more than half of sites, especially at the 100-year flood and beyond.

portrait photo of Frances Davenport
Frances Davenport

“This study shows that taking into account different physical processes can improve flood risk assessment,” said co-author Frances Davenport, a postdoctoral research fellow in CSU’s Department of Atmospheric Science. “Importantly, this result suggests both a need and opportunity to develop new methods of flood frequency assessment that will more accurately reflect flood risk in a warming climate.”

The study findings have important implications for estimating flood frequencies into the future, as climate change pushes conditions in snowmelt-dominated watersheds toward increased rainfall.

“How the 100-year flood will evolve in the future due to climate change is one of the most important unanswered questions in water resources management,” said co-author Daniel Wright, an associate professor in Civil and Environmental Engineering at University of Wisconsin-Madison. “To answer it, we need to focus on the fundamental science of how the water cycle, including extreme rainstorms and snow dynamics, will continue to change in a warming climate.”

The study team hopes that this research is useful to engineers, who rely on accurate estimates of flood frequencies when building bridges and other infrastructure. Although many engineers realize that there is a problem with the conventional way of estimating flood frequencies, this study provides new insights into the level of inaccuracy that results.

“In practice, the role of different mechanisms has often been ignored in deriving the flood frequencies,” said Yu, a Maki postdoctoral research associate at DRI. “This is partly due to the lack of physics-based understanding of historic floods. In this study, we showed that neglecting such information can result in uncertainties in estimated flood frequencies which are critical for infrastructure.”

The full study, “Diverse Physical Processes Drive Upper-Tail Flood Quantiles in the US Mountain West,” is published in Geophysical Research Letters.

This project was funded by the DRI’s Maki Postdoctoral Fellowship, U.S. National Science Foundation Hydrologic Sciences Program (award number EAR-1749638), and Stanford University. Study authors included Guo Yu (DRI/University of Wisconsin-Madison), Daniel Wright (University of Wisconsin-Madison), and Frances Davenport (Stanford University and Colorado State University).