A probabilistic approach to inundation prediction for decision making in coastal communities

Session: Coastal Resilience in the Face of Change (1)

Kyla Semmendinger, Cornell University, [email protected]
Scott Steinschneider, Department of Biological and Environmental Engineering, Cornell University, [email protected]

Abstract

In 2017, water levels on Lake Ontario reached the highest level recorded in a century. High water levels inundated homes and businesses throughout the summer and left others vulnerable to flash floods during storm surge and high wave events, causing millions of dollars in damages. The event was complicated by the fact that a new water level management plan went into effect for Lake Ontario at the beginning of 2017, and shoreline communities are concerned how the new plan impacts their flood risk. In this work, we develop a parcel-level flood risk tool tailored for shoreline stakeholders. The tool couples elevation information at the parcel level with a statistical characterization of flood risk based on regulated water levels, storm surge, and wave activity. We incorporate uncertainty from all data sources to establish probabilistic bounds of inundation, providing a conservative measure of risk for shoreline parcels. The tool is validated using data collected in a survey distributed to landowners during the 2017 water event. The goal of the tool is to provide community planners and property owners with a probabilistic assessment of parcel-level flood risk to help inform flood-risk reduction investments in the aftermath of the record 2017 event.