The FLOod Probability Interpolation Tool (FLOPIT): Improving Spatial Flood Probability Quantification and Communication Through Higher Resolution Mapping

TL;DR

This paper introduces FLOPIT, a tool that creates continuous flood probability maps from existing flood zone data, enhancing the accuracy and detail of flood risk communication for better decision-making.

Contribution

FLOPIT is a novel interpolation method that transforms flood zone data into detailed probability maps, improving flood risk assessment and communication.

Findings

FLOPIT produces more detailed flood probability maps from existing data.

Discrete flood zones underestimate actual flood probabilities.

Application to US locations demonstrates improved risk communication.

Abstract

Understanding flood probabilities is essential to making sound decisions about flood-risk management. Many people rely on flood probability maps to inform decisions about purchasing flood insurance, buying or selling real-estate, flood-proofing a house, or managing floodplain development. Current flood probability maps typically use flood zones (for example the 1 in 100 or 1 in 500-year flood zones) to communicate flooding probabilities. However, this choice of communication format can miss important details and lead to biased risk assessments. Here we develop, test, and demonstrate the FLOod Probability Interpolation Tool (FLOPIT). FLOPIT interpolates flood probabilities between water surface elevation to produce continuous flood-probability maps. We show that FLOPIT can be relatively easily applied to existing datasets used to create flood zones. Using publicly available data from the Federal Emergency Management Agency (FEMA) flood risk databases as well as state and national datasets, we produce continuous flood-probability maps at three example locations in the United States: Houston (TX), Muncy (PA), and Selinsgrove (PA). We find that the discrete flood zones generally communicate substantially lower flood probabilities than the continuous estimates.