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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A database of global storm surge reconstructions

Storm surges are among the deadliest coastal hazards and understanding how they have been affected by climate change and variability in the past is crucial to prepare for the future. However, tide gauge records are often too short to assess trends and perform robust statistical analyses. Here we use a data-driven modeling framework to simulate daily maximum surge values at 882 tide gauge locations across the globe. We use five different atmospheric reanalysis products for the storm surge reconstruction, the longest one going as far back as 1836. The data that we generate can be used, for example, for long-term trend analyses of the storm surge climate and identification of regions where changes in the intensity and/or frequency of storms surges have occurred in the past. It also provides a better basis for robust extreme value analysis, especially for tide gauges where observational records are short. The data are made available for public use through an interactive web-map as well as a public data repository.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Modeling assessment of storm surge in the Salish Sea

Here we present a modeling assessment of storm surge in the Salish Sea—a large and complex estuarine system in the Pacific Northwest that is exposed to extratropical cyclones. A method was developed to systematically identify storm surge events in the Salish Sea based on water level records at four representative tidal gauges in the Salish Sea. The maximum storm surge distribution in the Salish Sea was calculated based on model simulations of 34 major historical storm events from 1980 to 2016. The model simulations were validated by field observations of water level and velocity using a suite of model performance metrics. Model results suggest that the maximum storm surge in the Salish Sea is generally in the range of 0.8 m–1.03 m and is heterogeneous in space. The model results also show a strong north-south gradient with larger surge magnitudes occurring in the north that is primarily caused by the southerly wind in winter months. The sensitivity analysis of wind forcing suggested that storm surge in the Salish Sea is dominated by the remote surge from the Pacific Ocean, and the local wind contributes up to approximately 20% of the surge variability. This paper systematically elucidates storm surge characteristics and potential risk in the Salish Sea.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Long-term trends in storm surge climate derived from an ensemble of global surge reconstructions

We address the challenge, due to sparse observational records, of investigating long-term changes in the storm surge climate globally. We use two centennial and three satellite-era daily storm surge time series from the Global Storm Surge Reconstructions (GSSR) database and assess 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. 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.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Projecting U.S. coastal storm surge risks and impacts with deep learning

Storm surge is one of the deadliest hazards posed by tropical cyclones (TCs), yet assessing its current and future risk is difficult due to the phenomenon’s rarity and physical complexity. Recent advances in artificial intelligence applications to natural hazard modeling suggest a new avenue for addressing this problem. We develop a deep learning storm surge model to efficiently estimate coastal surge risk in the United States from 900 000 synthetic TC events, accounting for projected changes in TC behavior and sea levels. The derived historical 100 year surge (the event with a 1% yearly exceedance probability) agrees well with historical observations and other modeling techniques. When coupled with an inundation model, we find that heightened TC intensities and sea levels by the end of the century result in a 50% increase in population at risk. Key findings include markedly heightened risk in Florida, and critical thresholds identified in Georgia and South Carolina.

RAFT↗
DOE OSTI2024

Future projections of storm surge in Hurricane Katrina and sensitivity to meteorological forcing resolution

In this study, we investigated whether and how the storm surge induced by Hurricane Katrina could change if it occurs in a future warmer climate, and the sensitivity of the changes to atmospheric forcing resolution. Climate model simulations of Hurricane Katrina at 27 km, 4.5 km, and 3 km resolutions were used to drive storm surge simulations in historical and future climates using the ADvanced CIRCulation (ADCIRC) model. We found that peak surge height increased significantly in the future with all forcing resolutions. However, the future projection is 22% greater in the 3 km forcing, typical of regional climate models, compared to the 27 km forcing, typical of state-of-the-art global climate models. Additionally, the spatial extent of the future change is highly sensitive to forcing resolution, extending most broadly under the 27 km forcing. Furthermore, we found that storm surge duration decreases in the future with all forcing resolutions due to increasing TC translation speed and decreasing ocean lifetime. However, the future change in the surge duration is sensitive to the forcing resolution, decreasing by 31% in the 27 km forcing and 6% in the 3 km forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Storm surges and extreme sea levels: Review, establishment of model intercomparison and coordination of surge climate projection efforts (SurgeMIP).

Coastal flood damage is primarily the result of extreme sea levels. Climate change is expected to drive an increase in these extremes. While proper estimation of changes in storm surges is essential to estimate changes in extreme sea levels, there remains low confidence in future trends of surge contribution to extreme sea levels. Alerting local populations of imminent extreme sea levels is also critical to protecting coastal populations. Both predicting and projecting extreme sea levels require reliable numerical prediction systems. The SurgeMIP (surge model intercomparison) community has been established to tackle such challenges. Efforts to intercompare storm surge prediction systems and coordinate the community 's prediction and projection efforts are introduced. An overview of past and recent advances in storm surge science such as physical processes to consider and the recent development of global forecasting systems are briefly introduced. Selected historical events and drivers behind fast increasing service and knowledge requirements for emergency response to adaptation considerations are also discussed. The community 's initial plans and recent progress are introduced. These include the establishment of an intercomparison project, the identification of research and development gaps, and the introduction of efforts to coordinate projections that span multiple climate scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Seamless Projections of Global Storm Surge and Ocean Waves Under a Warming Climate

For coastal adaptation purposes, it is important to estimate the climate related changes in extreme sea levels due to storm surges and ocean waves, in addition to mean sea level rise. In this study, we provide the first consistent and continuous estimation of projected changes in global storm surges and ocean waves from the past to the warmer future, based on an extremely high resolution global climate model. The spatial pattern in the trend of annual maximum sea surface heights and wave heights is predominantly driven by changes in tropical cyclone (TC) frequency. In the western North Pacific, future TC frequencies are projected to decrease, and the annual maximum sea surface heights and wave heights show decreasing trends (-20 cm/century and -200 cm/century). Although highly intense tropical cyclones are enhanced in the warmer climate, highly extreme storm surges and wave heights do not necessarily increase due to the large natural variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Characterizing the Non-linear Interactions Between Tide, Storm Surge, and River Flow in the Delaware Bay Estuary, United States

Low-lying coastal areas in the mid-Atlantic region are prone to compound flooding resulting from the co-occurrence of river floods and coastal storm surges. To better understand the contribution of non-linear tide-surge-river interactions to compound flooding, the unstructured-grid Finite Volume Community Ocean Model was applied to simulate coastal storm surge and flooding in the Delaware Bay Estuary in the United States. The model was validated with tide gauge data in the estuary for selected hurricane events. Non-linear interactions between tide-surge-river were investigated using a non-stationary tidal analysis method, which decomposes the interactions’ components at the frequency domain. Model results indicated that tide-river interactions damped semidiurnal tides, while the tide-surge interactions mainly influenced diurnal tides. Tide-river interactions suppressed the water level upstream while tide-surge interaction increased the water level downstream, which resulted in a transition zone of damping and enhancing effects where the tide-surge-river interaction was prominent. Evident compound flooding was observed as a result of non-linear tide-surge-river interactions. Furthermore, sensitivity analysis was carried out to evaluate the effect of river flooding on the non-linear interactions. The transition zone of damping and enhancing effects shifted downstream as the river flow rate increased.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Hurricane wind field representation shapes storm surge and building-scale flood hazard estimates

Coastal flood hazard estimates rely on precise hurricane wind forecasts to assess damage and risk. Here, we demonstrate that errors in hurricane wind field representation can lead to significant biases in storm surge and property-level damage estimates. Using Hurricane Ian (2022) as a case study, we compare widely used parametric, reanalysis, and hybrid wind datasets. Improved wind field accuracy reduces storm surge and damage estimate bias by up to 70\%. Our results underscore the importance of accurately predicting hurricane wind structure in hazard assessments.

Coastal Flooding↗
DOE OSTI2019

Cross-scale modeling of storm surge, tide, and inundation in Mid-Atlantic Bight and New York City during Hurricane Sandy, 2012

Driven by high-resolution NAM (North America Mesoscale) and ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric fields, a 3-D unstructured grid SCHISM (Semi-implicit Cross-scale Hydroscience Integrated System Model) was applied to simulate total water level in the Mid-Atlantic Bight during Hurricane Sandy in 2012. The simulated storm surge, tide, significant wave height, and wave peak period results compared favorably with NOAA observations along US East Coast, Long Island Sound, and New York Harbor. The maximum total water level at The Battery in the New York Harbor was accurately simulated with an absolute error of less than 0.08 m (out of 2.9 m peak surge) and a timing difference less than 10 min, which includes the effects on the order of 0.1–0.3 m (5–10%) from the coastal setup induced by the surface waves. The scenario comparisons of (1) “NAM” versus “ECMWF”, (2) “2-D” versus “3-D”, and (3) “with” versus “without” wind wave model were examined. The 3-D barotropic model forced by ECMWF including the effects of wind waves performs the best, attributed to wave-induced radiation stress and reduction of bottom stress when the 3-D version is used. Simultaneously, the ELCIRC-Sub model, using a 5-m topography/bathymetry sub-grid and a regular 200-m resolution finite volume computational model grid, was developed to simulate the street-level inundation in New York City. The momentum and mass fluxes calculated by the coarser base grid model were effectively coupled with the sub-grid so that running a full-blown high-resolution base model is not required. The ELCIRC-Sub model uses MPI (Message Passing Interface) parallel computing to enlarge the coverage of the land surface and an efficient non-linear solver to improve the accuracy of the wetting-and-drying processes. The temporal comparisons of modeled water level with NOAA's tidal stations and USGS' rapid-deployed gauges showed overall performance with an average error of 0.1 m. Particularly, ELCIRC-Sub captured the profile of the highest peak surge (3.9 m), which rose 2 m within 3 hour at Kings Point, NY. The spatial comparisons of the modeled peak water level at 80 surveyed locations showed an average error less than 0.13 m. The modeled maximum inundation extent also matched well with an 80% hit rate against FEMA's Hurricane Sandy maximum flooding extent.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Storm Surge Modeling as an Application of Local Time-Stepping in MPAS-Ocean

This paper presents the first practical application of local time-stepping (LTS) schemes in the Model for Prediction Across Scales-Ocean (MPAS-O). We use LTS schemes in a single-layer, global ocean model that predicts the storm surge around the eastern coast of the United States during Hurricane Sandy. The variable-resolution meshes used are of unprecedentedly high resolution in MPAS-O, containing cells as small as 125 m wide in Delaware Bay. It is shown that a particular, third-order LTS scheme (LTS3) produces sea-surface height solutions that are of comparable quality to solutions produced by the classical four-stage, fourth-order Runge-Kutta method (RK4) with a uniform time step on the same meshes. Furthermore, LTS3 is up to 35% faster in the best cases considered, where the number of cells using the coarse time-step relative to those using the fine time-step is as low as 1:1. This shows that LTS schemes are viable for use in MPAS-O with the added benefit of substantially less computational cost. The results of these performance experiments inform us of the requirements for efficient mesh design and configuration of LTS regions for LTS schemes. In particular, we see that for LTS to be efficient on a given mesh, it is important to have enough cells using the coarse time-step relative to those using the fine time-step, typically at least 1:5 to see an increase in performance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Comparing emulation methods for a high‐resolution storm surge model

Abstract Realistic simulations of complex systems are fundamental for climate and environmental studies. Large computer systems are often not sufficient to run sophisticated computational models for large numbers of different input settings. Statistical surrogate models, or emulators, are key tools enabling fast exploration of the simulator input space. Gaussian processes have become standard for computer simulator emulation. However, they require careful implementation to scale appropriately, motivating alternative methods more recently introduced. We present a comparison study of surrogates of the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) simulator—the simulator of choice for government agencies—using four emulation approaches: BASS; BART; SEPIA; and RobustGaSP. SEPIA and RobustGaSP use Gaussian processes, BASS implements adaptive splines, and BART is based on ensembles of regression trees. We describe the four models and compare them in terms of computation time and predictive metrics. These surrogates use proven and distinct methodologies, are available through accessible software, and quantify prediction uncertainty. Our data cover millions of response values. We find that SEPIA and RobustGaSP provide exceptional predictive power, but cannot scale to emulate experiments as large as the one considered in this paper as effectively as BASS and BART.

Hutchings, Grant↗
DOE OSTI2023

Interacting Effects of Watershed and Coastal Processes on the Evolution of Compound Flooding During Hurricane Irene

Abstract In low‐lying estuarine regions, compound flooding (CF) is caused by the co‐occurrence of extreme precipitation, river flooding and storm surge. In recent decades, there has been a rise in the frequency and intensity of pluvial‐coastal CF events in different parts of the U.S. due to the increased frequency of intense precipitation and storm surge events. However, in estuarine and deltaic regions, the CF characteristics depend mainly on the storm tide and river flow interaction. Understanding how the fluvial‐coastal CF may respond to changes to watershed and estuarine characteristics is essential for future CF hazard prediction. This study examined two critical processes: (a) the interplay between antecedent soil moisture conditions and peak river flow, and (b) how the impact of sea level rise (SLR) on storm surge and river flood distribution alters the CF in complex estuaries. As the study area, we selected the Delaware Bay and River, a shallow and convergent estuary in the US Mid‐Atlantic region—where flood hazards during a CF can become more significant than the surge and river flood processes occurring in isolation. For the focal event for the study, we selected Hurricane Irene (2011) because it reportedly produced the most extreme CF over the past two decades in the same region. Ultimately, our results illustrated that the potential changes to the catchment and bay characteristics from the global temperature increase and SLR could significantly modulate the fluvial‐coastal CF variability. The potential increase in global temperature and rainfall intensity might not always exacerbate the CF.

54 ENVIRONMENTAL SCIENCES↗