CAREER: Risk-Based Methods for Robust, Adaptive, and Equitable Flood Risk Management in a Changing Climate

Purdue University, West Lafayette IN

Investigators

Abstract

Many natural hazards (e.g., floods, heat waves, etc.) are expected to become more frequent and severe under climate change. However, there is still considerable uncertainty about the rate and extent of contributing factors like sea level rise and the resulting changes in the hazards themselves, such as increases in the average intensity of tropical cyclones. This uncertainty leaves policymakers unsure of what conditions to plan for, leading to the possibility of catastrophes if the future turns out to be more extreme than expected. On the other hand, preparing for a worst-case scenario that never comes to pass may require overinvestment of scarce resources that could have been allocated to other societal and economic concerns. Research also shows that socially vulnerable and marginalized communities bear a disproportionate share of risks associated with natural hazards. The goal of this Faculty Early Career Development (CAREER) grant is to improve decision-makers' ability to manage risk from extreme events by (i) better quantifying natural hazards risks and (ii) identifying risk-informed, adaptive, and equitable management strategies. While the tools and methods developed during this project will be applicable to multiple natural hazards, the scope and motivation of the project are to develop, validate, and apply them in the context of storm surge, riverine, and pluvial (i.e., rainfall) flooding in coastal Louisiana. Unique datasets and state-of-the-art modeling capabilities will be leveraged to better characterize the joint risk of flooding from these sources and predict how the hazard will evolve over time under shifting landscapes (e.g., land subsidence, erosion, changes to vegetation associated with saltwater intrusion) and climate change-related environmental forcings (e.g., sea level rise, changes to tropical cyclone characteristics). A multi-resolution, multi-model framework and artificial intelligence will permit estimation of compound flood hazard in a large ensemble of future scenarios, which will then be applied to an existing structure-level risk model used in Louisiana’s Coastal Master Plan. Adaptive risk management strategies that balance economic efficiency and equity, and which are robust to uncertainties and varied operational definitions of equity, will be identified using methods for decision-making under deep uncertainty (DMDU). Educational components of the project will focus on increasing the adoption of DMDU methods and risk analysis, and helping STEM students and practitioners to translate natural hazards research into real-world policy impact. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.