High tide flooding (HTF) already affects traffic in many US coastal areas, but the issue will worsen significantly in the future. While studies show that large storm surge events threaten to be ever more costly, less damaging, but more frequent HTF events remain understudied and potentially carry a comparable economic impact. This study advances our understanding of the risks and impacts of HTF on vulnerable traffic corridors using hourly tide gauge water levels, sea-level rise projections, and link-level spatial analysis. It is the first study to estimate HTF economic impacts for varying levels of intervention, including reasonably anticipated driver-initiated rerouting and ancillary protection of adjacent property. The 2020 annual national-level costs of $1.3 to $1.5 billion will increase to $28 to $37 billion in 2050 and $220 to $260 billion in 2100 for medium to high greenhouse gas (GHG) emissions scenarios, respectively. Total costs over the century are $1.0 to $1.3 trillion (discounted 3%). Additional cost-effective protection by building sea walls or raising road surfaces could significantly reduce 2100 costs to $61 to $78 billion, but there remain many barriers to adopting least-cost adaptation decisions, and these gains may only be realized with careful planning and information sharing.

Drinking water utilities rely on samples collected from the distribution system to provide assurance of water quality. If a water contamination incident is suspected, samples can be used to determine the source and extent of contamination. By determining the extent of contamination, the percentage of the population exposed to contamination, or areas of the system unaffected can be identified. Using water distribution system models for this purpose poses a challenge because significant uncertainty exists in the contamination scenarios (e.g., injection location, amount, duration, customer demands, contaminant characteristics). This article outlines an optimization framework to identify strategic sampling locations in water distribution systems. The framework seeks to identify the best sampling locations to quickly determine the extent of the contamination while considering uncertainty with respect to the contamination scenarios. The optimization formulations presented here solve for multiple optimal sampling locations simultaneously and efficiently, even for large systems with a large uncertainty space. These features are demonstrated in two case studies.