Storm Surge Model Research Articles

Hurricane surge and inundation in the Bahamas, part 1: Storm surge model

AbstractA storm surge model has been developed as part of a pilot study for The Commonwealth of the Bahamas. The hydrodynamic model, TELEMAC‐2D, is used to simulate the response of water level to tide and the wind and atmospheric pressure fields of hurricanes and subsequent inundation over land. The model is used by The Bahamas Department of Meteorology to forecast storm surge and flooding over the islands of Grand Bahama and Eleuthera for incoming hurricanes to assist in preparation for and management of hurricane surge events and has been used in a flood risk assessment, reported in a companion paper. The model has been optimised to run quickly while also resolving the bathymetry and topography that affect the development and propagation of storm surge. Wind fields are generated within TELEMAC‐2D based on hurricane warning bulletins. The model has been validated for its representation of water level and against tide gauge measurements during four historical hurricanes: Irene, Sandy, Matthew and Dorian. Factors contributing to uncertainty in forecast predictions are discussed and recommendations are provided to improve the performance in future. The pilot study provides a template for future expansion to cover the other inhabited islands of The Bahamas.

Storm surge modelling along European coastlines: The effect of the spatio-temporal resolution of the atmospheric forcing

The spatio-temporal resolution of atmospheric forcing plays a key role in the accuracy of simulated storm surges with hydrodynamic numerical models. Here, we generate five hydrodynamic hindcasts of coastal storm surges along the European Atlantic and the Mediterranean Sea coasts, forced with atmospheric fields of varying temporal (hourly and daily) and spatial (0.25° to 2°) resolution since 1940. Our results, that are validated with insitu tide gauge observations, show that storm surges obtained with daily forcing underestimate the magnitude of coastal extreme sea level events by up to 50% compared to hourly simulations and observations. Nevertheless, low-resolution simulations capture the temporal variability of storm surges, including strong episodes. Furthermore, taking advantage of the consistent set of coastal storm surge hindcasts, we demonstrate that storm surges forced with daily mean atmospheric fields, when bias corrected via quantile mapping, provide accurate values of daily maxima as calculated by a high-resolution hindcast. This transformation paves the way to obtain daily maxima storm surge estimates from low-resolution atmospheric fields, as those typically provided by large-scale and global climate models, at a lower computational cost.

Application of GIS Spatial Analysis for the Assessment of Storm Surge Inundation Risks in the Guangdong–Macao–Hong Kong Great Bay Area

This study evaluates the storm surge inundation risk in three anthropogenically infilled estuaries—Xichong, Renshan, and Kaozhouyang—located in the Guangdong–Macao–Hong Kong Great Bay Area, China. By integrating GIS spatial analysis with storm surge modeling, we conducted 204 numerical experiments to simulate storm surge inundation under varying typhoon intensities and astronomical tide conditions. Results revealed that coastal terrain plays a crucial role in influencing storm surge levels and inundation extents. Specifically, the pocket-shaped terrain in the Renshan and Kaozhouyang estuaries amplified storm surges, resulting in higher inundation levels compared to the relatively open terrain of Xichong. Furthermore, anthropogenically reclaimed land in these estuaries appear to be particularly vulnerable to storm-induced inundation. Overall, this study underscores the importance of considering coastline morphology and the anthropogenic modifications of coastal terrain in storm surge risk assessments, offering valuable insights for disaster prevention and mitigation strategies. The use of ArcGIS spatial analysis coupled with storm surge modeling, facilitated by high-resolution DEMs, provides a statistical risk assessment of inundation. However, more complex flooding dynamics models need to be developed, particularly when terrestrial bottom friction information, which is heavily modified by human activities, can be accurately incorporated.

Subtle Land Subsidence Elevates Future Storm Surge Risks Along the Gulf Coast of the United States

AbstractWe developed a robust InSAR processing strategy that can effectively mitigate severe decorrelation noise in a large volume of InSAR data. We mapped the average land subsidence rate (2017–2020) over the 131,572 km2 Upper Texas and Louisiana coasts from Sentinel‐1 data, with ∼2 mm/yr accuracy based on independent GPS and tide gauge validation at 189 locations. The improved InSAR observations reveal widespread subsidence that was previously undetected in coastal wetlands and rural areas with small communities. Our InSAR surface deformation map is at the spatial scale that overlaps with the scale of hydrodynamic model grids. This allows us to integrate InSAR observations into operational storm surge models to analyze future flooding risks due to relative sea level change. We found that these subtle millimeter‐to‐centimeter subsidence features can substantially increase hurricane‐induced inundation, and passive flood mapping (known as the “bathtub” approach) can lead to inaccurate flood risk predictions.

Typhoons and Tigers—flood risk in the Sundarbans

The many small inhabited islands of the Sundabarn region in north east India and Bangladesh, are subject to sea water flooding during cyclones. The objective is to predict flood risk so that improvements to flood defences can be prioritised. There are a few records of flood heights at a very limited set of locations, but there are long term data on cyclones that can be used to drive a simulation model to estimate flood risk at any points in the Sundarbans. The cyclone data is used to fit a stochastic model for cyclones. The cyclone model is combined with a deterministic storm surge model that provides sea level at the boundary of the Sundarbans. A hydraulic routing model, MIKE–21, is then used to predict water levels over a grid of interior points. The combined deterministic surge and routing models are approximated by a regression type model, so the stochastic simulation can quickly generate thousand of years of flood events. Results from a simulation are presented.

Effects of tropical cyclone intensity on spatial footprints of storm surges: an idealized numerical experiment

Abstract Storm surges caused by tropical cyclones (TCs) have long ranked first among all types of marine disasters in casualties and economic losses, and can lead to further regional exacerbation of consequences stemming from these losses along different coastlines. Understanding the spatial footprints of storm surges is thus highly important for developing effective risk management and protection plans. To this end, we designed an ideal storm surge model based on FVCOM to explore the relationship between TC intensity and the spatial footprint of storm surges, and its intrinsic mechanism. The spatial footprints of both positive and negative storm surges were positively correlated with TC intensity; however, the latter was more sensitive to the intensity when the TC intensity is weaker than CAT3 TC’s. The average positive storm surge footprint of CAT1 was 574 km, with CAT3 and CAT5 increasing by 6% and 25%, respectively, compared to CAT1. The average spatial footprint of the negative storm surge of CAT1 was 1,407 km, with CAT3 and CAT5 increasing by 18% and 29%, respectively, compared to CAT1. The decomposition and mechanism analysis of the storm surge show that the main contributing component of the total surge at the south end of the storm’s landfall and during the time of the forerunner was the Ekman surge, whereas the contribution of the normal surge component to the north and during the time of the main surge and resurgence was dominant. In addition, not all the spatial footprints of the storm surge components increased with the TC intensity, as the total surge did, similar to the Ekman surge. These quantitative analyses and intrinsic mechanisms provide a theoretical basis for predicting and evaluating storm surge risks.

Influence of Grid Resolution and Assimilation Window Size on Simulating Storm Surge Levels

Grid resolution and assimilation window size play significant roles in storm surge models. In the Bohai Sea, Yellow Sea, and East China Sea, the influence of grid resolution and assimilation window size on simulating storm surge levels was investigated during Typhoon 7203. In order to employ a more realistic wind stress drag coefficient that varies with time and space, we corrected the storm surge model using the spatial distribution of the wind stress drag coefficient, which was inverted using the data assimilation method based on the linear expression Cd = (a + b × U10) × 10−3. Initially, two grid resolutions of 5′ × 5′ and 10′ × 10′ were applied to the numerical storm surge model and adjoint assimilation model. It was found that the influence of different grid resolutions on the numerical model is almost negligible. But in the adjoint assimilation model, the root mean square (RMS) errors between the simulated and observed storm surge levels under 5′ × 5′ and 10′ × 10′ grid resolutions were 11.6 cm and 15.6 cm, and the average PCC and WSS values for 10 tidal stations changed from 89% and 92% in E3 to 93% and 96% in E4, respectively. The results indicate that the finer grid resolution can yield a closer consistency between the simulation and observations. Subsequently, the effects of assimilation window sizes of 6 h, 3 h, 2 h, and 1 h on simulated storm surge levels were evaluated in an adjoint assimilation model with a 5′ × 5′ grid resolution. The results show that the average RMS errors were 11.6 cm, 10.6 cm, 9.6 cm, and 9.3 cm under four assimilation window sizes. In particular, the RMS errors for the assimilation window sizes of 1 h and 6 h at RuShan station were 3.9 cm and 10.2 cm, a reduction of 61.76%. The PCC and WSS values from RuShan station in E4 and E7 separately showed significant increases, from 85% to 98% and from 92% to 99%. These results demonstrate that when the assimilation window size is smaller, the simulated storm surge level is closer to the observation. Further, the results show that the simulated storm surge levels are closer to the observation when using the wind stress drag coefficient with a finer grid resolution and smaller temporal resolution.

The Role of Advection in Storm Surge for Hurricane Michael (2018)

AbstractHurricane Michael (2018) made landfall near Mexico Beach, FL, as a Category 5 hurricane, with gauge‐measured water levels over 4 m. Wind and pressure fields were created by blending a parametric near‐field model with a gridded far‐field model. Winds and modeled water levels were well validated across Michael's large impact a‐ea. A detailed analysis of the coastal surge caused by Michael demonstrates that advection contributed significantly to Michael's highest water levels and the timing of the water level across a large portion of the Michael impact area. A momentum balance in a streamwise‐normal coordinate system demonstrates that the advection contributions due to spatial gradients in the flow are identified with streamwise convergence/expansion of the flow field (Bernoulli acceleration) and curvature in the flow field (centrifugal acceleration). These effects are created by the regional geometry and the storm's wind field and are most likely to affect back barrier water levels and along curved coastlines. These findings provide an improved understanding of the role of advection in determining storm surge and, thus, the importance of including it in storm surge models.

Improved local time-stepping schemes for storm surge modeling on unstructured grids

This paper presents improved explicit local time-stepping (LTS) schemes of both first and second order accuracy for storm surge modeling. The two-dimensional shallow water equations are numerically solved on unstructured triangular meshes using finite volume method with Roe’s approximate Riemann solver. The LTS algorithms are designed based on explicit Euler and strong stability preserving Runge–Kutta time integration methods. A single-layer interface prediction–correction scheme is adopted to combine coarse and fine time discretization, further enhancing the stability of the LTS schemes, particularly at higher LTS levels and during long time simulations. An ideal numerical test validates the efficiency of the improved LTS models, revealing their capability to improve computational speed while preserving conservation properties and reducing accuracy loss as LTS levels increase. We further apply the LTS models to cross-scale simulations of storm surges in the Northwest Pacific. Results show that compared to the global time-stepping (GTS) models, the LTS models significantly boost computational speed by up to 37%, all while delivering equally reliable computational outcomes. With expanding high-resolution coastal data and the need for high-resolution modeling, the improved LTS models show great potential for cross-scale storm surge modeling.

Explanations for the positive storm surges on the left side of landfall typhoons in China

The coastal regions of Southeast China frequently experience unusual positive storm surges on the left side of landfalling typhoons, a phenomenon historically overlooked and inadequately explained by conventional circular wind field models. In this study, a high resolution, two-dimensional storm surge model based on ADCIRC along with tide gauge data were used to investigate the spatiotemporal distribution of these surges and proposes underlying mechanisms, informed by a comparative analysis of circular and ERA5 reanalysis wind fields during typical typhoon event 9711 Winnie. Analyzing tide gauge data spanning from 1986 to 2016, the study uncovers a distinct pattern of left-side positive storm surges along the southeastern coast, notably on the Fujian coast and within the Taiwan Strait, which are found to be comparable to those on the cyclone’s right side. The research also documents a significant escalation in both the frequency and intensity of these left-side surges over the past three decades. Simulation results highlights the inadequacies of circular wind field models in operational forecasting and emphasizes the necessity of accounting for topographic influences and the structural complexity of wind fields in storm surge predictions. This is particularly pertinent in semi-enclosed seas with intricate hydrodynamics, such as the Taiwan Strait. The insights gleaned from this study are pivotal for enhancing the real-time simulation and prediction of storm surges, which are vital for coastal safety and disaster prevention measures.

Predictability of Hurricane Storm Surge: An Ensemble Forecasting Approach Using Global Atmospheric Model Data

Providing storm surge risk information at multi-day lead times is critical for hurricane evacuation decisions, but predictability of storm surge inundation at these lead times is limited. This study develops a method to parameterize and adjust tropical cyclones derived from global atmospheric model data, for use in storm surge research and prediction. We implement the method to generate storm tide (surge + tide) ensemble forecasts for Hurricane Michael (2018) at five initialization times, using archived operational ECMWF ensemble forecasts and the dynamical storm surge model ADCIRC. The results elucidate the potential for extending hurricane storm surge prediction to several-day lead times, along with the challenges of predicting the details of storm surge inundation even 18 h before landfall. They also indicate that accurately predicting Hurricane Michael’s rapid intensification was not needed to predict the storm surge risk. In addition, the analysis illustrates how this approach can help identify situationally and physically realistic scenarios that pose greater storm surge risk. From a practical perspective, the study suggests potential approaches for improving real-time probabilistic storm surge prediction. The method can also be useful for other applications of atmospheric model data in storm surge research, forecasting, and risk analysis, across weather and climate time scales.

Comparative evaluation of meteorological inputs for improved storm surge modeling: A case study of tropical Cyclone Vayu

The selection of meteorological inputs in storm surge is crucial, with wind and pressure fields playing a significant role in energy transfer and the study area's bathymetry. While using observed track information for wind and pressure drop has been the standard approach for the past decade, recent studies have highlighted the need for atmospheric inputs from models like the Weather Research and Forecast model (WRF) for storm surge forecast. This study aims to compare the performance of a DELFT-3D FM storm surge model forced with inputs from IMD (India Meteorological Department) observed the best track and pressure drop (EXP-1) and wind and pressure fields from WRF (EXP-2) for Tropical Cyclone Vayu, which occurred in the southern Arabian Sea basin in June 2019. The study compares the simulated storm water levels and highlights the benefits of using time and space-varying wind and pressure input for improved surge representation. Results show that the WRF-DELFT setup outperforms the IMD-DELFT setup, particularly for tide gauge locations close to the storm eye. The simulated storm surge captures the intensified stage of Vayu and provides a more realistic representation than the model forced with IMD track data. However, biases and limitations, such as inadequate representation of land surface parameterization, are identified. The study suggests further exploring wave-induced effects on total water level and multiple cyclone scenarios to enhance wind speed and track displacement prediction accuracy and improved land-sea parameterization can help address these limitations.

Footprint of the air-sea momentum transfer saturation observed by ocean wave buoy network in extreme tropical cyclones

Tropical cyclones are one of the most destructive natural phenomena, causing tremendous coastal disasters worldwide. The maximum intensity of tropical cyclones is determined by momentum and heat transfer at the air-sea interface. Momentum transfer corresponds to the momentum loss of tropical cyclones and, consequently, to the underlying ocean's momentum gain causing extreme ocean waves and storm surges. Air-side observations of wind profiles and ocean-side observations of ocean subsurface currents show a slowdown of momentum transfer under high tropical cyclone wind speeds in the previous studies. However, there is still disagreement regarding the slowdown owing to lack of data. Here, we show momentum transfer under high wind speed conditions by observing ocean waves. Although ocean wave observations are highly spatially limited, we deployed a fleet of drifting ocean wave buoys covering the active area of tropical cyclones in the Western North Pacific. The buoy fleet captured extreme waves near the eye of the strongest category 5 tropical cyclone, indicating an ocean wave footprint of the momentum transfer saturation above surface wind speeds of 25 m/s. Our approach from ocean surface wave observation is a unique contribution to determination on air-sea momentum transfer slowdown under extreme wind speeds, which can compensate for the knowledge from conventional air and ocean-side observations. This finding advances tropical cyclones, extreme ocean waves, and storm surge modeling.

Advanced Sea Ice Modeling for Short-Term Forecasting for Alaska’s Coasts

Abstract In Alaska’s coastal environment, accurate information of sea ice conditions is desired by operational forecasters, emergency managers, and responders. Complicated interactions among atmosphere, waves, ocean circulation, and sea ice collectively impact the ice conditions, intensity of storm surges, and flooding, making accurate predictions challenging. A collaborative work to build the Alaska Coastal Ocean Forecast System established an integrated storm surge, wave, and sea ice model system for the coasts of Alaska, where the verified model components are linked using the Earth System Modeling Framework and the National Unified Operational Prediction Capability. We present the verification of the sea ice model component based on the Los Alamos Sea Ice Model, version 6. The regional, high-resolution (3 km) configuration of the model was forced by operational atmospheric and ocean model outputs. Extensive numerical experiments were conducted from December 2018 to August 2020 to verify the model’s capability to represent detailed nearshore and offshore sea ice behavior, including landfast ice, ice thickness, and evolution of air–ice drag coefficient. Comparisons of the hindcast simulations with the observations of ice extent presented the model’s comparable performance with the Global Ocean Forecast System 3.1 (GOFS3.1). The model’s skill in reproducing landfast ice area significantly outperformed GOFS3.1. Comparison of the modeled sea ice freeboard with the Ice, Cloud, and Land Elevation Satellite-2 product showed a mean bias of −4.6 cm. Daily 5-day forecast simulations for October 2020–August 2021 presented the model’s promising performance for future implementation in the coupled model system. Significance Statement Accurate sea ice information along Alaska’s coasts is desired by the communities for preparedness of hazardous events, such as storm surges and flooding. However, such information, in particular predicted conditions, remains to be a gap. This study presents the verification of the state-of-art sea ice model for Alaska’s coasts for future use in the more comprehensive coupled model system where ocean circulation, wave, and sea ice models are integrated. The model demonstrates comparable performance with the existing operational ocean–ice coupled model product in reproducing overall sea ice extent and significantly outperformed it in reproducing landfast ice cover. Comparison with the novel satellite product presented the model’s ability to capture sea ice freeboard in the stable ice season.

A Major Improvement of Atmospheric Wave Boundary Layer Model for Storm Surge Modeling by Including Effect of Wave Breaking on Air–Sea Momentum Exchange

Abstract Accurate estimation of the wind stress under extreme conditions is crucial for modeling storm surges and storm waves, which is important to the development of a warning system for coastal disaster prevention. The problem, however, is highly challenging owing to the presence of complex ocean surface processes under the action of unusually strong wind. In this study, the existing atmospheric wave boundary layer model is significantly enhanced by including various effects of wave breaking. Both the effect of wave breaking on the dissipation of energy and its effect on the transfer of momentum within the atmospheric boundary layer are carefully formulated. The wind stress coefficients obtained with the enhanced model are shown to be in good agreement with the measurements in not only deep but also shallow waters. The enhanced atmospheric wave boundary layer model is coupled with ocean wave as well as circulation models to simulate typhoon-induced storm surges and storm waves in the Pearl River delta region. The computational results show that the coupled model with improved evaluation of the wind stress is substantially advantageous when compared with existing approaches.

Projecting Changes in the Drivers of Compound Flooding in Europe Using CMIP6 Models

AbstractWhen different flooding drivers co‐occur, they can cause compound floods. Despite the potential impact of compound flooding, few studies have projected how the joint probability of flooding drivers may change. Furthermore, existing projections may not be very robust, as they are based on only 5 to 6 climate model simulations. Here, we use a large ensemble of simulations from the Coupled Model Intercomparison Project 6 (CMIP6) to project changes in the joint probability of extreme storm surges and precipitation at European tide gauges under a medium and high emissions scenario, enabled by data‐proximate cloud computing and statistical storm surge modeling. We find that the joint probability will increase in the northwest and decrease in most of the southwest of Europe. Averaged over Europe, the absolute magnitude of these changes is 36%–49% by 2080, depending on the scenario. The large‐scale changes in the joint probability of extreme storm surges and precipitation are similar to those in the joint probability of extreme wind speeds and precipitation, but locally, differences can exceed the changes themselves. Due to internal climate variability and inter‐model differences, projections based on simulations of only 5 to 6 randomly chosen CMIP6 models have a probability of higher than 10% to differ qualitatively from projections based on all CMIP6 simulations in multiple regions, especially under the medium emissions scenario and earlier in the twenty‐first century. Therefore, our results provide a more robust and less uncertain representation of changes in the potential for compound flooding in Europe than previous projections.

Prediction of storm surge in the Pearl River Estuary based on data-driven model

Storm surges, a significant coastal hazard, cause substantial damage to both property and lives. Precise and efficient storm surge models are crucial for long-term risk assessment and guiding emergency management decisions. While high-fidelity dynamic models offer accurate predictions, their computational costs are substantial. Hence, recent efforts focus on developing data-driven storm surge surrogate models. This study focuses on the Pearl River Estuary in Guangdong Province. Initially, the dynamic ADvanced CIRCulation (ADCIRC) model was utilized to construct storm surge data for 16 historical typhoons, serving as training, validation, and testing data for data-driven models. Subsequently, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Informer deep learning (DL) models were employed for forecasting of storm surge over the next 1h, 3h, 6h, 12h, and 18h. Finally, Shapley Additive exPlanations (SHAP) values were used for interpretability analysis of the input factors across different models. Results indicated that the proposed DL storm surge prediction model can effectively replicate the dynamic model’s simulation results in short-term forecasts, significantly reducing computational costs. This model offers valuable scientific assistance for future coastal storm surge forecasts in the Greater Bay Area.

Storm surge modeling in the AI era: Using LSTM-based machine learning for enhancing forecasting accuracy

Physics simulation results of natural processes usually do not fully capture the real world. This is caused for instance by limits in what physical processes are simulated and to what accuracy. In this work we propose and analyze the use of an LSTM-based deep learning network machine learning (ML) architecture for capturing and predicting the behavior of the systemic error for storm tide forecast models with respect to real-world water elevation observations from gauge stations during hurricane events. The overall goal of this work is to predict the systemic error of the physics model and use it to improve the accuracy of the simulation results post factum (i.e., to correct the model bias). We trained our proposed ML model on a dataset of 61 historical storms in the coastal regions of the south and southeastern U.S. and we tested its performance in bias correcting modeled water level data predictions from Hurricane Ian (2022). We show that our model can consistently improve the forecasting accuracy for Hurricane Ian – unknown to the ML model – at the majority of gauge station coordinates. Moreover, by examining the impact of using different subsets of the initial training dataset, containing a number of relatively similar or different hurricanes in terms of hurricane track, we found that we can obtain similar quality of bias correction by only using a subset of six hurricanes. This is an important result that implies the possibility to apply a pre-trained ML model to real-time hurricane forecasting results with the goal of bias correcting and improving the forecast accuracy. The presented work is an important first step in creating a bias correction system for real-time storm tide forecasting applicable to the full simulation area. It also presents a highly transferable and operationally applicable methodology for improving the accuracy in a wide range of physics simulation scenarios beyond storm tide forecasting.

Influence of bed roughness parameter in storm surge modeling along the east coast of India

Influence of bed roughness parameter in storm surge modeling along the east coast of India

A direct insertion technique to assimilate sea surface height into a storm surge model

Natural hazards like hurricanes, cyclones, and typhoons frequently cause major economic and livelihood loss in coastal areas around the world. However, accurately predicting sea surface height and flood inundation extent at the operational level remains a challenge. This work tests the hypothesis that assimilating satellite-like sea surface observations within a storm surge model can improve the estimation of storm surge dynamics for operational forecasting. A synthetic experiment is proposed based on Hurricane Irene, which moved along the United States East Coast in 2011. With a focus on the Chesapeake Bay area, a framework is developed to simulate coastal sea surface height using a storm surge model and merging synthetic satellite observations. To represent the “truth” of the system, a hindcast simulation was run for the Hurricane Irene event. Different satellite revisit times are considered to assess the improvement over the numerical model open-loop (i.e., no data assimilation) simulation. Results indicate that the assimilation of synthetic sea surface height observations has the potential to reduce errors and improve the accuracy in the hydrodynamic model simulations by up to 25% (in terms of correlation coefficient), especially when the satellite has short revisit times (i.e., daily, half-daily and flood tides). Such improvements are consistent regardless of the bias sign in the satellite observations and the location, which is fundamental in an operational setting where errors in satellite observations are often unknown.