Changes in the seasonal sea level cycle (SSLC) can modulate the flooding risk along coastlines. Here, we use harmonic analysis to quantify changes in the amplitude and phase of the annual component of the sea level cycle at 663 tide gauge locations along the global coastline where long records are available. We identify coastal hotspots by applying clustering methods revealing coherent regions with similar patterns of variability in the annual sea level cycle (ASLC). Results show that for most tide gauges the annual amplitude reached its maximum after 1970 and its peak typically occurs during the fall season of the respective hemisphere. Many tide gauges exhibit non-stationarity in the annual cycle in terms of amplitude and/or phase. For example, at 125 tide gauges we find significant trends in the amplitude (either increasing or decreasing) while several sites (36 in total), mostly in the Mediterranean and around Pacific islands, experienced phase changes leading to shifts in the timing of the peak of the annual cycle by more than a month. Our results highlight the importance of accounting for potential non-stationarity in seasonal mean sea level (MSL) cycles along coastlines.

Natural hazards such as floods, hurricanes, heatwaves, and wildfires cause significant economic losses (e.g., agricultural and property damage) as well as a high number of fatalities. Natural hazards are often driven by univariate or multivariate hydrometeorological drivers. Therefore, it is crucial to understand how and which hydrometeorological variables (i.e., drivers) combine to contribute to the impacts of these hazards. Additionally, when multiple drivers are associated with a hazard, traditional univariate risk assessment approaches are insufficient to cover the full spectrum of impact-relevant conditions originating from different combinations of multiple drivers. Based on historical socioeconomic loss data, we develop an impact-based approach to assess the influence of different hydrometeorological drivers on the impacts caused by different hazard event types. We use the Spatial Hazard Events and Losses Database for the United States (SHELDUS™) to identify the historical hazard events that caused socioeconomic impacts (property and crop damage, injuries, and fatalities) in our case study area, Miami-Dade County, in south Florida. For 9 different hazard types, we obtained data for 13 hydrometeorological drivers from historical in-situ observations and reanalysis products corresponding to the timing and locations of the hazard events found in the SHELDUS database. The relative importance of each hazard driver in generating impacts and the frequency of multiple drivers was then assessed. We found that many high-impact events were caused by multiple hydrometeorological drivers (i.e., compound events). For example, 61% of the recorded flooding events were compound events rather than univariate hazards and these contributed 99% of total property damage and 98.2% of total crop damage in Miami-Dade County. For several hazards, such as hurricanes/tropical storms and wildfires, all the events that caused damage are classified as compound events in our framework. Our findings emphasize the benefit of including socioeconomic impact information when analyzing hazard events, as well as the importance of analyzing all relevant hydrometeorological drivers to identify compound events.

Natural hazards such as coastal and river floods, tornadoes, droughts, heatwaves, wildfires, and landslides cause significant economic losses (e.g., agriculture and property damage) and notable counts of fatalities. While natural hazards are often considered to be caused by a single climatic driver (e.g., coastal flood from storm surge only), they can be associated with the combined occurrence of multiple drivers (e.g., coastal flood driven by storm surge and precipitation). Defining whether the climatic drivers (e.g., precipitation, temperature, or wind) are extreme enough to turn the hazards into disasters is crucial for estimating disaster risks. To date, extreme events are often defined using the block maxima or peaks over threshold methods ignoring the effects of the built environment and socio-economic conditions. However, a hazard with the same magnitude can cause very different impacts in regions with varying built environments and socio-economic conditions. Additionally, when multiple climatic drivers are involved, traditional methods of defining extreme events (block maxima and peaks over threshold) are challenging to apply. In this research, we employ an impact-based approach and define critical thresholds of climatic drivers for 12 different hazards, across the US southeast coast. We use the SHELDUS database (CEMHS, 2020) to identify historical hazard events that caused socio-economic losses (property and crop damage) and identify corresponding magnitudes of climatic drivers from historical in-situ observations and reanalysis datasets from 1979 to 2019. We then identify thresholds of climatic drivers for impact events where only one or multiple drivers were involved. These impact-based thresholds can be used, for example, to backfill loss databases (where impact information was not available) and to project potential impacts into the future using projections of the different climatic drivers.

Atmospheric rivers (ARs) effect inland hydrological impacts related to extreme precipitation. However, little is known about the possible coastal hazards associated with these events. Here we elucidate high-tide floods (HTFs) and storm surges during ARs on the US West Coast during 1980—2016. HTFs and landfalling ARs co-occur more often than expected from random chance. Between 10%—63% of HTFs coincide with landfalling ARs, depending on location. However, only 2%—15% of ARs coincide with HTFs, suggesting that ARs typically must co-occur with anomalously high tides or mean sea levels to cause HTFs. Storm surges during ARs are interpretable in terms of local wind, pressure, and precipitation forcing: meridional wind and barometric pressure are the primary drivers of storm surge, but precipitation can also make secondary contributions. This study highlights the relevance of ARs to coastal impacts, clarifies the drivers of storm surge during ARs, and identifies future research directions.

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 nodal and perigean tidal cycles influence extreme sea levels at 234 global tide gauges out of a total of 344. Results show regions where the amplitudes of the modulations are particularly relevant in the 100-year return sea level; the eastern coast of China, the northwestern and northeastern coasts of Australia, the northeast coast of Pacific, and the eastern coast of Europe, reaching up to ~22 cm in western France. 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 nodal/perigean 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 (nodal or perigee) 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, areal change is often smaller 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.

We investigate trends in the magnitude and frequency of extreme storm surge events at 320 tide gauges across the globe from 1930, 1950, and 1980 to present. We use two centennial and three satellite-era daily storm surge time series from the Global Storm Surge Reconstructions (GSSR) database. Before calculating trends, we perform change point analysis to identify and remove data where inhomogeneities in atmospheric reanalysis products could lead to spurious trends in the storm surge data. Even after removing unreliable data, the database still extends existing storm surge records by several decades for most of the tide gauges. Storm surges derived from the centennial 20CR and ERA-20C atmospheric reanalyses show consistently significant positive trends along the southern North Sea and the Kattegat Bay regions during the periods from 1930 and 1950 onwards and negative trends since 1980 period. When comparing all five storm surge reconstructions and observations for the overlapping 1980-2010 period we find overall good agreement, but distinct differences along some coastlines, such as the Bay of Biscay and Australia. We also assess changes in the frequency of extreme surges and find that the number of annual exceedances above the 95th percentile has increased since 1930 and 1950 in several regions such as Western Europe, Kattegat Bay, and the US East Coast.

Low-lying coastal zones are prone to flooding from multiple drivers such as storm surge (oceanographic), excessive river discharge (fluvial), and/or surface runoff (pluvial). The flooding impacts can be exacerbated, depending on local characteristics, when flooding is intensified by concurrent (or successive) occurrence of multiple drivers known as ‘compound flooding’. Recently, compound flooding drivers are becoming more frequent and intense leading to more adverse impacts. In this study, we carry out a continental scale analysis for the CONUS coastline at locations with sufficiently long overlapping records to characterize the changes in dependence and co-occurrence between the compound flooding drivers over time. We also investigate the changes in dependence over time during tropical and extratropical seasons. Lastly, we assess how the dependence structure varies with time. We use observations (gauge records) for the analysis. Dependence between different pairs is assessed using co-occurrence counts and statistical measures for dependence (Kendall’s rank correlation coefficient, τ). The dependence structures (particularly the tails of bivariate distributions) are compared using Kullback–Leibler (KL) Divergence to assess if there are significant changes in tails of bivariate distributions over time. This analysis provides a comprehensive characterization of changes in compound flooding potential around the CONUS coastline. This will provide insights on where and how compound flooding potential has changed over time to be incorporated in flood risk assessments and planning.