Michelle A. Hummel

TL;DR: In this article, Hummel et al. used geographic information systems to assess the exposure of wastewater infrastructure to various sea level rise projections at the national level, and then estimated the number of people who would lose wastewater services, which could be more than five times as high as previous predictions of direct flooding due to sea-level rise.

TL;DR: In this paper, the authors report a large-n survey experiment on San Francisco Bay Area residents to investigate how providing spatially-resolved risk information to individuals shapes their climate risk perceptions in the context of sea-level rise.

TL;DR: In this paper, Hummel et al. used geographic information systems to assess the exposure of wastewater infrastructure to various sea level rise projections at the national level, and then estimated the number of people who would lose wastewater services, which could be more than five times as high as previous predictions of direct flooding due to sea-level rise.

TL;DR: In this article, the authors performed a series of numerical simulations for San Francisco Bay to examine two shoreline scenarios and examined two short and long-term sea-level variations, including a half-meter perturbation, with duration ranging from 2 days to permanent (i.e., sea level rise) and the extent of coastal flooding increased with the duration of the High Water Level event.

TL;DR: In this paper, the authors used statistical cluster analysis to identify similarities in community exposure to flooding hazards for a suite of sea level rise and storm scenarios, which can serve as a data-driven foundation to help communities identify other communities with similar adaptation challenges and to enhance regional efforts that aim to facilitate adaptation planning and investment prioritization.