H132-07
The illustrated recipe to make stormwater infrastructure resilient for the climate of the 21st century

Monday, 14 December 2020: 04:24
Virtual
Tania Paola Paola Lopez-Cantu and Constantine Samaras, Carnegie Mellon University, Civil and Environmental Engineering, Pittsburgh, PA, United States
Abstract:
In the U.S., there is evidence that more rainfall is falling every year during extreme events since 1958. By the end of the 21st century, rainfall extremes – events defined by greater than XX-year average recurrence intervals, are projected to happen more frequently due to climate change. For infrastructure in urban environments, changing rainfall patterns mean that infrastructure needs to operate outside the range of conditions that they were originally designed for – conditions that do not reflect observed and future changes. Recognizing this gap, substantial efforts have been devoted to providing future climate projections at more actionable spatial and temporal resolutions for infrastructure design, developing methods to incorporate non-stationarity, among others. Nonetheless, there are still many challenges to incorporating the much-needed changes in the planning, engineering, and deployment of infrastructure. Some of these challenges include ease of use and access to climate projections or large uncertainties in the magnitude and timing of future changes, to mention a few. By analyzing engineering standards, more than 80 city-level climate adaptation plans and projections from multiple downscaled climate projections datasets widely used in planning and impacts assessments, we argue that increasing stormwater infrastructure resilience to future rainfall extremes requires four fundamental steps: 1) Analyze existing infrastructure vulnerabilities, 2) Use multiple sources of downscaled climate projections and available methods to document uncertainties, 3) Structure future changes in the context of existing rainfall information used in engineering design, and 4) Apply robust-decision methods to identify low-regret adaptation strategies.