Extreme Precipitation in Illinois: Trend Estimation and Relation with Large-Scale Circulation and Humidity

Author

Andrew Blair Paxton, Southern Illinois University CarbondaleFollow

Date of Award

9-1-2021

Degree Name

Master of Science

Department

Geography and Environmental Resources

First Advisor

Schoof, Justin

Abstract

Extreme precipitation in Illinois contributes to impacts across diverse landscapes, posing threats to agriculture in rural areas and infrastructure in urban centers. Previous studies have reported an increase in the frequency of heavy precipitation in the region and projected its amplification under climate change. However, these findings are often characterized by inconsistent and/or inappropriate approaches for estimating historical trends and their significance and often lack process-based understanding regarding future changes in extreme event climatology. This study aims to obtain robust regional extreme precipitation trends and relate those trends to large-scale circulation and humidity. The climatology and trends of daily extreme precipitation are established by applying a peaks-over-threshold approach to the newly developed NOAA NCEI nClimGrid-D dataset which includes daily precipitation totals at 5-km resolution. For trend estimation, we use Theil-Sen estimation with three approaches designed to emphasize correction of inflation in the significance of the estimated trends: (1) a “naïve” approach in which we simply consider the direct output of the Theil-Sen method and assess significance using a traditional Mann-Kendall test, (2) an approach based on a modified Mann-Kendall test to account for serial autocorrelation in the assessment of significance, and (3) an approach that also controls for the false discovery rate associated with a large number of tests by considering field significance. To relate these trends to large scale drivers, a multivariate self-organizing map is constructed based on standardized 500 mb geopotential height and 850 mb specific humidity obtained from the ECMWF ERA-5 reanalysis dataset. We use a Monte Carlo experiment to identify weather types most associated with extreme precipitation in the area. Temporal and spatial characteristics of the identified weather types are then analyzed to better understanding their role in changes in the frequency of extreme precipitation events across the region. As expected, the results indicate a stark contrast between the naive and more complex approaches for significance testing, where controlling for autocorrelation and test multiplicity reduces the spatial extent of significant trends across all extreme precipitation thresholds. Extreme precipitation in Illinois is found to be associated with a small number of specific weather types characterized by distinct patterns of geopotential height and humidity. Furthermore, the weather types most frequently associated with extreme precipitation are increasing in frequency, suggesting that changes in atmospheric circulation related to moisture transport and convergence are a major contributor to changes in extreme precipitation in Illinois.