Session: ASME Undergraduate Student Design Expo

Paper Number: 173400

Wind-Induced Compound Flood Modeling and Sensitivity Analysis for Pensacola Bay, Florida 

Compound flooding, which results from the combined effects of storm surge, precipitation, and riverine discharge, poses a growing threat to coastal regions. Accurate prediction of such events requires models that account for the full range of environmental drivers. Traditional flood models often neglect dynamic atmospheric variables such as wind, despite their known influence on storm trajectories, surface water transport, and precipitation patterns. This study expands an existing compound flood model by incorporating wind speed and wind direction to evaluate how wind influences precipitation distribution and flood extent in coastal watersheds.

Using HEC-RAS 6.4 and environmental datasets sourced from the National Oceanic and Atmospheric Administration (NOAA), we incorporated hourly wind speed and direction data collected during Hurricane Sally into an established model of Pensacola Bay, located in the Florida Panhandle, along the northern Gulf of America.   The primary objective of this study is to evaluate how wind influences precipitation distribution and flood behavior when interacting with other hydrologic drivers. Wind direction was modeled as a vector forcing influencing the storm path and spatial precipitation distribution, while wind speed was treated as a scalar variable that modifies storm intensity and water surface movement.

To evaluate the influence of wind, we compared the original baseline model—without wind inputs—to an enhanced version that incorporated wind data. This direct comparison allowed us to quantify the difference in flood extent, inundation depth, and spatial precipitation patterns attributable solely to wind forcing. The analysis showed that the inclusion of wind significantly affected projected outcomes, especially in low-lying coastal regions aligned with dominant wind flows.

To quantify model sensitivity, we conducted a two-part analysis: (1) a wind speed sensitivity test, applying incremental adjustments to baseline values to assess impacts on flow rate, inundation depth, and flooded area; and (2) a directional sensitivity test, rotating wind input across angular intervals to observe changes in precipitation accumulation and water routing within the watershed. Results demonstrated that both wind variables had a measurable impact on model output, particularly in low-lying regions exposed to onshore winds. In specific scenarios, directional changes shifted precipitation hotspots and altered floodplain extents.

The inclusion of wind significantly enhanced the model’s ability to capture storm-induced variability and localized risk, yielding more accurate and spatially resolved flood projections. From a mechanical engineering perspective, these improvements contribute directly to infrastructure risk assessments, flood barrier design, and emergency planning strategies. Moreover, the approach demonstrates the value of multidisciplinary modeling in developing robust, wind-sensitive flood prediction tools for coastal resilience.

This study demonstrates that wind is a critical component of compound flood modeling and underscores the need for multi-parameter environmental modeling in coastal engineering. The methodology developed here provides a scalable, wind-integrated modeling workflow that can be applied to other flood-prone regions facing increased climate variability.

Presenting Author: Gillian Londa Home address

Presenting Author Biography: Gillian Londa is a Mechanical Engineering student entering her fourth semester, currently conducting research with the Resilient Infrastructure and Disaster Response (RIDER) Program at the FAMU-FSU College of Engineering under Dr. Ebrahim. Her work focuses on applying machine learning to flood modeling and wind analysis for improved disaster response. Previously, she conducted sustainability and infrastructure research at the Lawrence Berkeley Lab. Gillian is the Vice President of the Engineering Club at San Jacinto College, a member of the Honors College, and participates in Rice University’s Take Flight program to expand her research opportunities. She aims to advance data-driven solutions for environmental resilience.

Authors:

Gillian Londa Home address
Husnain Tansar Florida State University
Ebrahim Ahmadisharaf Florida State University