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Abstract We demonstrate that long‐term tidally induced changes in extreme sea levels affect estimates of major flood hazard in a predictable way. Long‐term variations in tides due to the 4.4 and 18.6‐year cycles influence extreme sea levels at 380 global tide gauges out of a total of 581 analyzed. Results show coherent regions where the amplitudes of the modulations are particularly relevant in the 100‐year return sea level, reaching more than 20 cm in some regions (western Europe, north Australia, and Singapore). We identify locations that are currently in a positive phase of the modulation and therefore at a higher risk of flooding, as well as when (year) the next peak of the long‐term tidal modulations is expected to occur. The timing of the peak of the modulation is spatially coherent and influenced by the relative importance of each cycle (4.4 or 18.6‐year) over the total amplitude. An evaluation of four locations suggests that the potentially flooded area in a 100‐year event can vary up to ∼45% (in Boston) as a result of the long‐term tidal cycles; however, the flooded area varies due to local topography and tidal characteristics (6%–13%). We conclude that tidally modulated changes in extreme sea levels can alter the potentially inundated area in a 100‐year event and that the traditional, fixed 100‐year floodplain is inadequate for describing coastal flood risk, even without considering sea‐level rise. 

Abstract. In coastal regions, compound flooding can arise from a combination of different drivers such as storm surges, high tides, excess river discharge, and rainfall. Compound flood potential is often assessed by quantifying the dependence and joint probabilities of the flood drivers using multivariate models. However, most of these studies assume that all extreme events originate from a single population. This assumption may not be valid for regions where flooding can arise from different generation processes, e.g., tropical cyclones (TCs) and extratropical cyclones (ETCs). Here we present a flexible copula-based statistical framework to assess compound flood potential from multiple flood drivers while explicitly accounting for different storm types. The proposed framework is applied to Gloucester City, New Jersey, and St. Petersburg, Florida as case studies. Our results highlight the importance of characterizing the contributions from TCs and non-TCs separately to avoid potential underestimation of the compound flood potential. In both study regions, TCs modulate the tails of the joint distributions (events with higher return periods) while non-TC events have a strong effect on events with low to moderate joint return periods. We show that relying solely on TCs may be inadequate when estimating compound flood risk in coastal catchments that are also exposed to other storm types. We also assess the impact of non-classified storms that are neither linked to TCs or ETCs in the region (such as locally generated convective rainfall events and remotely forced storm surges). The presented study utilizes historical data and analyzes two populations, but the framework is flexible and can be extended to account for additional storm types (e.g., storms with certain tracks or other characteristics) or can be used with model output data including hindcasts or future projections. 

The datafiles in this dataset contain tropical or extratropical cyclone track information. Each datafile name includes the name of city. The datafile contains track information (location and time) for all cyclones that pass within 1000 km of the named city. Each datafile contains a single variable, which is a datatype called: structure in Matlab (or, dictionary). Each entry in the dictionary is an cyclone track. The entries in the dictionary are in chronological order, based on the starting date of the track. The relevant fields in the dictionary are the center latitude, center longitude, and date. The extratropical tracks were identified by applying Mike Bauer’s MCMS tracking tool to ERA5 reanalysis data. The tropical cyclone tracks are from HURDAT2. Full details can be found in the Booth et al. (2021) article referenced with this dataset. For more information contact James Booth: jbooth@ccny.cuny.edu 

Data Description Created by: James Booth Contact: jbooth@ccny.cuny.edu This data is used in the following journal article: Towey, K. L., Booth, J. F., A. Rodríguez Enríquez, T. Wahl, 2022: Tropical cyclone storm surge probabilities for the east coast of the United States: A cyclone-based perspective. Nat. Hazards Earth Syst. Sci., 22, 1287–1300, https://doi.org/10.5194/nhess-22-1287-2022. Brief: Each .mat file contains two variables: alldate - a vector of dates using the Matlab datenum format surgenon – a vector of de-trended storm surge values (units: Meters) De-trending is carried out by removing a 365-day running average. For all time points within the first half-year and the final half-year, the running average centered on the middle of the respective year is removed. A full description of the calculations used in generating the data is in the journal article. If you would like the data in a different file type, contact James Booth at jbooth@ccny.cuny.edu 

Each netcdf (.nc) file contains the location of atmospheric blocks over North America and its surrounding oceans. The data are gridded in latitude and longitude. The block data is simply a mask, with 1s in locations where a block has been detected and zeros elsewhere. A full description of the calculations used in generating the data is in the related journal article. If you would like the data in a different file type, contact James Booth. Contact: jbooth@ccny.cuny.edu 

Abstract. To improve our understanding of the influence of tropicalcyclones (TCs) on coastal flooding, the relationships between storm surgeand TC characteristics are analyzed for 12 sites along the east coast of theUnited States. This analysis offers a unique perspective by first examiningthe relationship between the characteristics of TCs and their resultingstorm surge and then determining the probabilities of storm surge associatedwith TCs based on exceeding certain TC characteristic thresholds. Usingobservational data, the statistical dependencies of storm surge on TCs areexamined for these characteristics: TC proximity, intensity, path angle, andpropagation speed, by applying both exponential and linear fits to the data.At each tide gauge along the east coast of the United States, storm surge isinfluenced differently by these TC characteristics, with some locations morestrongly influenced by TC intensity and others by TC proximity. Thecorrelation for individual and combined TC characteristics increases whenconditional sorting is applied to isolate strong TCs close to a location.The probabilities of TCs generating surge exceeding specific return levels(RLs) are then analyzed for TCs passing within 500 km of a tide gauge, wherebetween 6 % and 28 % of TCs were found to cause surge exceeding the1-year RL. If only the closest and strongest TCs are considered, thepercentage of TCs that generate surge exceeding the 1-year RL is between 30 % and 70 % at sites north of Sewell's Point, VA, and over 65 % atalmost all sites south of Charleston, SC. When examining storm surgeproduced by TCs, single-variable regression provides a good fit, whilemulti-variable regression improves the fit, particularly when focusing on TCproximity and intensity, which are, probabilistically, the two mostinfluential TC characteristics on storm surge. 

Abstract Storm surge is a weather hazard that can generate dangerous flooding and is not fully understood in terms of timing and atmospheric forcing. Using observations along the Northeast United States, surge is sorted based on duration and intensity to reveal distinct time-evolving behavior. Long-duration surge events slowly recede, while strong, short-duration events often involve negative surge in quick succession after the maximum. Using Lagrangian track information, the tropical and extratropical cyclones and atmospheric blocks that generate the surge events are identified. There is a linear correlation between surge duration and surge maximum, and the relationship is stronger for surge caused by extratropical cyclones as compared to those events caused by tropical cyclones. For the extremes based on duration, the shortest-duration strong surge events are caused by tropical cyclones, while the longest-duration events are most often caused by extratropical cyclones. At least half of long-duration surge events involve anomalously strong atmospheric blocking poleward of the cyclone, while strong, short-duration events are most often caused by cyclones in the absence of blocking. The dynamical influence of the blocks leads to slow-moving cyclones that take meandering paths. In contrast, for strong, short-duration events, cyclones travel faster and take a more meridional path. These unique dynamical scenarios provide better insight for interpreting the threat of surge in medium-range forecasts.