877 Incorporating Climate Model Projections into the Development of IDF Estimates for the Kansas City Area

Wednesday, 9 January 2019
Hall 4 (Phoenix Convention Center - West and North Buildings)
Madison Crowl, Univ. of Kansas, Lawrence, KS; and J. K. Roundy

There is building evidence that climate change is causing an intensification of precipitation patterns. Locations around the world can expect to experience more intense precipitation events. Engineers must be able to account for this future climatic uncertainty in their designs in order develop sustainable and resilient systems. Intensity-Duration-Frequency (IDF) estimates, developed by the National Oceanic and Atmospheric Administration (NOAA), are used across the United States for engineering design. These estimates were developed under the assumption of a stationary climate (with respect to precipitation intensity). However, research has shown that this assumption may lead to underestimation of extreme events. Climate models are a useful tool for understanding future climate and estimating future trends in precipitation quantity and temporal distribution on larger time scales. The Coupled Model Intercomparison Project (CMIP) developed by the World Climate Research Programme (WRCP) provide average monthly precipitation projections from various climate models. For the Kansas City area, these models show no significant trend in yearly precipitation. However, they do show seasonal variability, with significantly increasing precipitation in some months. While Kansas City gage data show a corresponding increase in extreme events, the climate models do not directly describe this precipitation intensity at the temporal scale imperative for engineering designs. This work aims to bridge the gap between climate model projections and IDF curves needed for engineering design by creating nonstationary estimates of IDF curves. This is done by exploring methods to incorporate CMIP5 climate projections into the Annual Maximum Series (AMS) and then use statistical methods to create IDF estimates for the Kansas City area that incorporate climate uncertainty. Nonstationary AMS estimates are developed by incorporating trends in both historical gage data and CMIP5 projections. These nonstationary IDF estimates are developed using the NOAA Annual Maximum Series (AMS) for several gauges in the Kansas City Area using L-moment statistics to fit Generalized Extreme Value (GEV) distributions. Analysis, implications and limitations of using this approach for engineering design will be discussed.
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