Flooding, one of the most frequently occurring hazards, results in billions of dollars of damage each year. Adaptation strategies such as seawalls and levees are designed to minimize some of these costs, mitigating the impacts of rare flooding events. The optimal design of these interventions is often determined using historical climate extremes that assume no change over time. However, observed increases to the severity and frequency of extreme events mean that past conditions can no longer be reliable estimates of future events. Flood risk management strategies designed to minimize the impacts of present-day flooding may, therefore, drive unintended consequences under future climate conditions.
This project explores the transfer of flood risk in an urban setting by crossing changes in climate extremes with infrastructure modifications. Our study site, the San Francisquito Creek, runs through five municipalities, all of which have a large variation in wealth. Local authorities are currently deciding how to best manage sediment which has accumulated at the upstream dam while avoiding an increase in downstream flood threats. To evaluate the risk of riverine flooding, we merge probabilistic modeling of flood drivers with a hydraulic model to determine along-river water levels. This technique allows us to identify and probabilistically quantify the dominant drivers of flooding under a suite of future flood management strategies and climate conditions. We find that infrastructure projects built to mitigate present-day flooding transfer flood risk to downstream locations where there is a higher percentage of low-income residents. Our study provides an impact centric framework that weighs the positive outcomes of flood mitigation strategies with the potential for negative socioeconomic ramifications, all of which are necessary to consider for sustainable and equitable flood risk management in an uncertain future.