Given the increasing intensity and frequency of flood events, and the casualties and cost associated with bridge collapse events, explaining the flood behavior for the collapse sites would be of great necessity. In this study, annual peak flows of two hundred and five watersheds, associated with two hundred and ninety-seven collapse sites, are analyzed. Generalized Extreme Value distribution together with other statistical analyses are used to derive and analyze the shape parameters of the distributions which represent the extremeness of flood events. Random forest mechanism is employed in order to identify the predictor variables (and the associated importance levels) for the shape parameters. Peak flows are also classified in order to find the extremes and the associated return periods. The results indicate that most of the bridge collapse sites across different physiographic regions, i.e., Appalachian Highland, Central Lowland, Coastal Plain, and Interior Highlands, exhibit common characteristics such as (a) variation of important predictor variables, (b) human interference, (c) extremeness of flood events similar to the regions with hydrologic heterogeneity, and (d) frequent occurrence of extreme flows. These results indicate a commonality in flood behavior, as stems from specific settings, for the collapse sites studied. The findings instigate the revisiting of the bridge design practices and guidelines and provide some basis to assess the risk of future collapse.

Bridge collapse risk can be evaluated more rigorously if the hydrologic characteristics of bridge collapse sites are demystified, particularly for peak flows. In this study, forty-two bridge collapse sites were analyzed to find any trend in the peak flows. Flood frequency and other statistical analyses were used to derive peak flow distribution parameters, identify trends linked to flood magnitude and flood behavior (how extreme), quantify the return periods of peak flows, and compare different approaches of flood frequency in deriving the return periods. The results indicate that most of the bridge collapse sites exhibit heavy tail distribution and flood magnitudes that are well consistent when regressed over the drainage area. A comparison of different flood frequency analyses reveals that there is no single approach that is best generally for the dataset studied. These results indicate a commonality in flood behavior (outliers are expected, not random; heavy-tail property) for the collapse dataset studied and provides some basis for extending the findings obtained for the 42 collapsed bridges to other sites to assess the risk of future collapses.Water 2020, 12, 52 2 of 25 are used to directly define an extreme value distribution; and peaks over-threshold (POT), in which distributions are fit to both the frequency of floods above a threshold and their magnitudes. The U.S. Water Resources Council requires the use of log-Pearson type 3 (LP3) distributions for the annual maxima approach, although the generalized extreme value distribution (GEV) seems to be the most widely used model for extreme events [7]. The major benefit of the GEV model is its ability to fit highly skewed data [7]. Therefore, recent hydrologic research has focused on explaining the parameters of the GEV distribution of streamflow extremes [8][9][10][11][12][13][14].If annual maximum exceedances are assumed to be GEV-distributed, the POT exceedances are assumed to be generalized Pareto (GP)-distributed [15], following recommendations in the field of statistics [16,17]. Fitting the GP distribution to exceedances over a high threshold and also estimating the frequency of exceeding the threshold by fitting a Poisson distribution allows for the simultaneous fitting of parameters concerning both the frequency and intensity of extreme events. Compared to the annual maxima approach, therefore, the main advantage of POT modeling is that it allows for a more rational selection of events to be considered as "floods" and is not confined to only one event per year. The POT approach considers a wide range of events and provides the possibility of controlling the number of flood occurrences to be included in the analysis by appropriate selection of the threshold. However, the POT approach remains under-employed mainly because of the complexities associated with the choice of threshold and the selection of criteria for retaining flood peaks (Lang et al. (1999)). Nonetheless, threshold selection is tightly linked to the choice of the process distribution, to t...

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This study explores the at‐a‐station hydraulic geometry (AHG) for reaches with bridge collapse events. Eighteen reaches in Eastern United States, thirteen reaches in Appalachian Highland, and five reaches in Coastal Plain are examined. The methodology applied for retrieving AHG uses LiDAR (Light detection and ranging) data, and the study results are checked for both hydraulic and geomorphic consistency. The resulting data set is composed of five to thirty‐five measurements of water surface width, mean depth, and mean velocity at each of the 181 cross‐sections. The exponents of the AHG relationships vary considerably. Nonetheless, for most of the cross‐sections, width responds more rapidly to changing discharge, and velocity exponents are less than the width and depth exponents combined. Wide shallow channels with highly erodible beds and/or banks, the ability to transport large bed materials, and the ability to attain a super‐critical condition—are the common profile extracted for most of the cross‐sections across all sites. A definitive AHG configuration is found for the sites with the least human interference. Comparatively low variation of bank‐full geometry is also found for the sites with the least human interference. The prevalence of low flows and/or lower return periods of heavy‐tail flows are also exhibited for most of the sites. The study results suggest that the stream channel instability can be reasonably understood and predicted from AHG particularly if human interference is limited within the watershed. These findings have implications not only for the study of the risk of bridge collapse and bridge design but also to characterize instability in a more rigorous and practical way.