Incorporating uncertainty from downscaled rainfall projections into climate resilience planning in US cities

The planning, design, and maintenance of stormwater infrastructure must be informed by changing rainfall patterns due to climate change. However, there is little consensus on how future climate information should be used, or how uncertainties introduced by use of different methods and datasets should be characterized or managed. These uncertainties exacerbate existing challenges to using climate information on local or municipal scales. Here we analyze major cities in the U.S., 48 of which developed climate adaptation and resilience plans. Given the prevalence of depth duration frequency (DDF) curves for planning infrastructure for rainfall, we then assessed the underlying climate information used in these 48 plans to show how DDF curves used for resilience planning and the resulting outcomes can be affected by stakeholders' methodological choices and datasets. For rainfall extremes, many resilience plans varied by trend detection method, data preprocessing steps, and size of study area, and all used only one of the available downscaled climate projection datasets. We evaluate the implications of uncertainties across five available climate datasets and show the level of climate resilience to extreme rainfall depends on the dataset selected for each city. We produce risk matrices for a broader set of 77 U.S. cities to highlight how local resilience strategies and decisions are sensitive to the climate projection dataset used in local adaptation plans. To help overcome barriers to using climate information, we provide an open dataset of future daily rainfall values for 2-, 5-, 10-, 25-, 50-, and 100 years annual recurrence intervals for 77 cities and compare resilience outcomes across available climate datasets that each city can use for comparison and for robust resilience planning. Because of uncertainty in climate projections, our results highlight the importance of no-regret and flexible resilience strategies that can be adjusted with new climate information.