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Abstract Particularly important to hurricane risk assessment for coastal regions is finding accurate approximations of return probabilities of maximum wind speeds. Since extremes in maximum wind speed have a direct relationship with minima in the central pressure, accurate wind speed return estimates rely heavily on proper modeling of the central pressure minima. Using the HURDAT2 database, we show that the central pressure minima of hurricane events can be appropriately modeled by a nonstationary extreme value distribution. We also provide and validate a Poisson distribution with a nonstationary rate parameter to model returns of hurricane events. Using our nonstationary models and numerical simulation techniques from established literature, we perform a simulation study to model returns of maximum wind speeds of hurricane events along the North Atlantic coast. We show that our revised model agrees with current data and results in an expectation of higher maximum wind speeds for all regions along the coast, with the highest maximum wind speeds occurring along the northeast seaboard. 

Abstract The Braiding Index (BI), defined as the average count of intercepted channels per cross‐section, is a widely used metric for characterizing multi‐thread river systems. However, it does not account for the diversity of channels (e.g., in terms of water discharge) within different cross‐sections, omitting important information related to system complexity. Here we present a modification ofBI,the Entropic Braiding Index (eBI), which augments the information content inBIby using Shannon Entropy to encode the diversity of channels in each cross section.eBIis interpreted as the number of “effective channels” per cross‐section, allowing a direct comparison with the traditionalBI. We demonstrate the potential of the ratioBI/eBIto quantify channel disparity, differentiate types of multi‐thread systems (braided vs. anastomosed), and assess the effect of discharge variability, such as seasonal flooding, on river cross‐section stability.