On Aug. 21, 2021, Waverly, Tennessee, received nearly one-third of its average annual rainfall in just a few hours — the resulting 17 inches of rain led to one of the deadliest flooding events on record.

In fact, Waverly faced some of the worst consequences of the severe rain and subsequent flooding that hit Central Tennessee in the summer of 2021. Unfortunately, the area was unprepared for the torrent that killed 21 people and caused over $100 million in property damage.

A combination of factors causes flooding, including rainfall, geographic location, the amount of moisture already in the environment, the flow of rivers and streams and surface runoff. And each factor has a direct impact on the severity of the flood. A variety of models can simulate these individually, but few studies have assessed the accuracy of a framework that captures the entire flood event.

To better understand the factors that contributed to the August 2021 floods, a multi-institutional team tested a new hierarchical flood modeling framework that can simulate the entire flood event and translate precipitation to inundation. One of the main questions with a multimodel approach is how errors propagate through coupled, or dependent, simulations.

In a study published in the Journal of Hydrology, researchers identified one of the most important sources of uncertainty, or error, in the flood models and demonstrated the accuracy and utility of the framework for predicting future floods.