A probabilistic approach to characterize the joint occurrence of two extreme precipitation indices in the upper Midwestern United States

Abstract

Extreme precipitation-related hazards like flash floods pose a widespread risk to humans and infrastructure around the world. In the current study, the Fisher information was applied to understand the nonstationarity of the extreme precipitation regimes, whereas copula was used to quantify the likelihood of joint occurrence of two extreme precipitation indices and associated risk assessment in the upper Midwestern United States (UMUS). The trend analysis revealed an increasing trend in 37% of the stations in heavy precipitation amount in the UMUS. The regime shift analysis showed the non-stationary nature of extreme precipitation in about half of the total stations in UMUS. Further, the bivariate analysis using copula demonstrated the risk of the joint occurrence of extreme precipitation indices potentially causing flash floods. The risk index analysis indicated about 28.8% of stations under moderate, 10.6% of stations under high and 0.4% of stations under very high risk of flash flooding. The results from the study can provide important insights for the (re)design of resilient and sustainable water infrastructure in the changing climate condition and can also inform managers and planners for better response and preparedness toward extreme precipitation-related hazards in this region. The results from this study can also help in a more accurate risk assessment, especially in the socio-economically vulnerable community.