We present a flood-risk assessment framework that integrates hazard, vulnerability, and exposure elasticities to identify locations where mitigation yields the greatest benefit. Using Fathom flood hazard data for Louisiana’s 64 parishes, the framework employs depth - frequency (stage - return period) curves fitted with generalized extreme value (GEV) distributions and Markov Chain Monte Carlo (MCMC) methods to characterize probabilistic flood behavior across coastal, fluvial, and pluvial sources.

To capture physical and social dimensions of risk, we disaggregate the National Structure Inventory (NSI) and synthesize household-level demographic data using IN-CORE to reflect race, income, insurance status, and housing tenure. Households are randomly allocated to structures at the block level via Monte Carlo simulation, enabling fine-grained estimation of damages.

We compute three elasticity metrics: (1) depth–damage elasticity (vulnerability), measuring the marginal change in damage from increased flood depth; (2) exposure elasticity, reflecting changes in affected population or assets with event magnitude; and (3) return-period–damage elasticity (hazard), quantifying how expected damages shift with changes in event frequency. Concave segments of damage and exposure curves indicate high-sensitivity areas where the marginal return on mitigation is greatest.

Standardized mitigation increments of 6, 12, and 18 inches demonstrate how small reductions in flood depth disproportionately increase return periods and lower damages, particularly where damage functions are steep. This technical methodology enables efficient targeting of flood resilience measures by mapping elasticity scores and identifying priority locations for investment.

By linking hazard modeling, synthetic populations, and probabilistic loss estimates, this approach advances equitable and cost-effective planning. It supports the goals of FEMA’s National Flood Insurance Program and Community Rating System by guiding communities toward mitigation strategies that maximize flood risk reduction and insurance premium benefits.