Climate change is anticipated to reduce collection system levels of service, due to increases in the frequency and intensity of heavy rainfall events. However, efforts to predict climate change impacts on collection system performance and plan future infrastructure investments are limited by large uncertainties in Global Climate Model (GCM) projections and downscaling methods. The objective of this study was to quantify the uncertainty associated with modeling collection system response to climate change using an ensemble of EPA-SWMM simulations forced by downscaled GCM projections. Using Milwaukee, WI as a case study, we estimated future temperature and precipitation time-series by applying statistical downscaled GCM output to historical ground observations. Statistical downscaling was performed on 28 CMIP6 ensemble members and 3 Shared Socio-economic Pathways (SSP2-4.5, SSP3-7.0, and SSP5-8.5) at mid-century and end-of-century. With a system-scale validated urban hydrologic model (SWMM), we evaluated the collection system performance under a wide range of temperature and precipitation forcing through event and continuous simulations. The results of this study quantify the uncertainty associated with modeling collection system climate change response, allowing utilities to understand the range of potential performance changes under future climates.
