CS28B - Concurrent Session 28B: Comparison of Two Stochastic Modeling Approaches for PFHA Development

Abstract Description: Probabilistic Flood Hazard Analysis (PFHA) for dam and levee safety is typically conducted using either stochastic rainfall-runoff models or inflow volume-based statistical methods. Stochastic rainfall-runoff models require expertise in storm typing, regional precipitation-frequency analysis, storm pattern development and selection, and rainfall-runoff model development, calibration, and validation. In contrast, inflow volume-based statistical methods can be used to develop probabilistic flood hazard curves with a lower level of effort and less expertise, although they rely heavily on high-quality, long-term at-site flow data, which is not always available.

When long-term, high-quality, unregulated at-site flow observations are not available, the stochastic rainfall-runoff approach to PFHA is the preferred method. Two widely used tools for performing this type of analysis are the U.S. Army Corps of Engineers (USACE) Risk Management Center (RMC) Rainfall-Runoff Frequency Tool (RRFT) and the Stochastic Event Flood Model (SEFM) developed by MGS Engineering.

This study presents two case studies highlighting the similarities and differences between RRFT and SEFM, using a rainfall-driven basin and a basin with both rainfall and snowmelt components. Both stochastic tools employ deterministic rainfall-runoff models while treating hydrometeorological inputs as random variables instead of fixed values. However, SEFM offers additional capabilities that make it more flexible and better suited for complex hydrologic conditions. While both models produce similar results in the rainfall-only basin, SEFM demonstrates a more robust and accurate performance in the snowmelt-driven basin.

This paper compares and contrasts the performance of RRFT and SEFM, offering recommendations on when each tool is most appropriate. The discussion also identifies opportunities for future improvements, emphasizing the potential benefits of integrating the strengths of both tools to enhance PFHA methodologies within USACE and beyond.