Wisconsin engineers create flood risk prediction tool for Lower Mississippi River Basin

Researchers at the University of Wisconsin have introduced a new computer model designed to predict escalating flood risk across the Lower Mississippi River Basin, accounting for extreme rainfall, river basin responses, and uncertainty in storm behavior. The model extends prior work in stochastic storm transposition and hydrologic simulation to large watershed scales, enabling probabilistic forecasts of flood risk under changing climate and weather patterns.

The model inputs include synthetic storm transpositions, where historical storms are “relocated” across the basin to simulate a wide range of possible rainfall-runoff scenarios, and hydrologic routing through the river network. The approach allows estimation not just of expected flood magnitudes, but distributions of extreme responses. This probabilistic framework helps identify areas where flooding frequency and severity may shift under future climate forcing.

One objective of the research is to improve flood risk maps beyond static 100-year flood boundaries by incorporating storm variability and spatial correlation of extreme events. In the Lower Mississippi Basin, the model suggests that even moderate storm shifts could push river levels into flood thresholds more often than historical baselines allow.

The new method builds upon prior work by UW’s Daniel Wright, whose RainyDay software uses stochastic storm transposition in smaller watersheds. That technique is now being scaled up to continental watersheds, such as the Mississippi, by adapting to large-scale landscape and climatic heterogeneity.

Model developers emphasize that the Lower Mississippi system’s size, multiple tributaries, and complex flood dynamics present challenges in calibration, routing, and uncertainty propagation. Nonetheless, this advance offers the potential to anticipate shifting flood risk zones and inform infrastructure design, levee planning, and resilience strategies in a basin long known for extreme flood variability.