The Seasonality and Climate Predictability of the Frequency of Extreme Flood Events Across the Contiguous United States
This paper investigates the seasonality of the frequency of extreme flood events across the United States (US) and outlines the impact of large-scale climate on its spatio-temporal variability. The analysis was conducted on 317 stream gages from the United States Geological Survey (USGS) Hydro-Climatic Data Network (HDCN) over a period of 68 years (1950-2017), resulting in the estimation of the counts of extreme floods (defined as the number of days in each month when the streamflow is greater than the 95th percentile of daily distribution) and the determination of the Markham’s seasonality index for it. 97% of the stations analyzed were found to exhibit seasonality in the counts of extreme floods, with 17% of the stations showing a high seasonality index of 0.8 to 1. It was also observed that the dominant season of extreme flood events occurred in the spring months of March-April-May with approximately 60% of the stations falling into this category and are typically located in the Midwest to Eastern regions of the US. The second dominant season occurs during the winter period of December-January-February with 26% of the stations in this category and is mostly seen in the West, Southeastern and East Coasts of the US. Inference models with the aid of Least Absolute Shrinkage and Selection Operator (LASSO) strategy using season-ahead predictors and Negative Binomial link function were also developed to deduce the effects of large-scale climate teleconnections on the rate of extreme flood occurrences. Results show that overall, approximately 62% of the stations are influenced by a selected set of 7 climate teleconnections. NINO3.4 and NINO1.2 (indices for El-Nino Southern Oscillation) are influencing factors in 32% and 36% of the stations, respectively. NAO (North Atlantic Oscillation) SNAO (Summer NAO) are significant in 23% and 19% of stations, respectively. AMO (Atlantic Multi-decadal Oscillation), PDO (Pacific Decadal Oscillation), and IPO (Inter-decadal Pacific Oscillation) are significant in 24%, 27%, and 22% of the stations respectively. In total, only about 6% of the stations are influenced by all seven climate indicators and these are in states including California, Idaho, Nevada, and North Dakota amongst others. These pre-season climate indicators can be used for developing forecasts of flood counts in the upcoming seasons, which then can be used for preparation and taking pre-emptive measures to mitigate flood damages.
Floods, Seasonality, Climate, Data analysis, Prediction model, Civil Engineering, Environmental Engineering, Other Civil and Environmental Engineering
City University of New York - City College
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