Abstract
Urban flood risks are often determined by the frequency analysis of observed rainfall data, communicated using isohyetal maps showing rainfall totals for a range of durations and recurrence intervals. However, to assess future changes in heavy rainfall, it is necessary to study the future projected rainfall time series. Impacts of climate change are typically assessed using climate projections based on global climate model (GCM) outputs and downscaled to finer temporal and spatial scales. The projected data, however, are not generated in a format that urban planners and engineers can easily use to design for future conditions. This research presents a method to analyze and express climate data in a format that can be readily used in hydrologic models to assess the effects of future extreme rainfall events. Future conditions’ climate data were analyzed using a weighted ensemble approach, which resulted in projected rainfall frequency estimates and their confidence limits. Two multimodel data sets were selected to illustrate this approach in Cook County, Illinois, which belongs to the Chicago metropolitan area. The first data set included statistical downscaling data based on the Intergovernmental Panel for Climate Change’s (IPCC) Coupled Model Intercomparison Project Phase 3 (CMIP3) data. The weighted ensemble analysis applied to this data set produced results that indicated significant increases in projected heavy rainfall. For example, for CMIP3 Scenario A2, for the late 21st century, the 100-year, 24-h rainfall in the northern parts of the county was 29% larger than the model-generated rainfall for the present time. For the same time horizon and scenario, the confidence interval based on projected data was 87% wider compared with that of the published source (NOAA Atlas 14), calculated using the past observed data. Also, equal-weight delta-corrected IPCC CMIP5-based dynamical downscaling data were applied to the same region for the mid-21st century, producing increases in heavy rainfall fairly similar to those of CMIP3.
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Acknowledgments
This study was funded by the National Oceanic and Atmospheric Administration, Climate Program Office under a grant from the Climate and Societal Interactions–Sectoral Applications Research Program (NOAA-OAR-CPO-2013-2003445) and by the Prairie Research Institute Matching Research Awards Program (MRAP) at the University of Illinois at Urbana-Champaign. The Oak Ridge Leadership Computing Facility supported the dynamical downscaling experiments at ORNL. Dr. David Lorenz (University of Wisconsin) provided statistically downscaled data and useful explanations used in this study. Zoe Zaloudek and Sara Olson from Illinois State Water Survey (ISWS) helped with graphical presentation of the results, and Lisa Sheppard (ISWS) provided editorial help.
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Journal of Hydrologic Engineering
Volume 23 • Issue 4 • April 2018
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©2018 American Society of Civil Engineers.
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Received: Mar 25, 2017
Accepted: Aug 22, 2017
Published online: Jan 18, 2018
Published in print: Apr 1, 2018
Discussion open until: Jun 18, 2018
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