Numerical Study on Storm Surge Level Including Astronomical Tide Effect Using Data Assimilation Method

Sensitivities of modelling storm surge to bottom friction, wind drag coefficient, and meteorological product in the East China Sea

Hurricane storm surge represents a significant threat to coastal communities around the world. Here, we use artificial neural network (ANN) models to predict storm surge levels using hurricane characteristics along the US Gulf and East Coasts. The ANN models are trained with storm surge levels from a hydrodynamic model and physical characteristics of synthetic hurricanes which are downscaled from National Centers for Environmental Prediction (NCEP) reanalysis using a statistical‐deterministic hurricane model. The ANN models are able to accurately predict storm surge levels with root‐mean‐square errors (RMSE) below 0.2 m and correlation coefficients > 0.85. The ANN models trained with the NCEP data also show satisfactory accuracy (RMSE below 0.7 m; correlation > 0.7) in predicting storm surge levels for hurricanes downscaled from future climate projections. Once trained, we use the ANN models to assess the sensitivity of storm surge levels to variations in hurricane characteristics and local geophysical features. Progressively stronger maximum wind speeds and larger outer radius sizes independently increase storm surge levels at all locations along the US East and Gulf Coasts. The response of storm surge levels to changes in hurricane translation speed, however, is found to be sensitive to coastal configuration, with increases in hurricane translation speed amplifying (reducing) storm surge levels in open ocean (semi‐enclosed) regions.

An evaluation of the effect of bottom friction, wind drag coefficient, and meteorological forcing is conducted using a tightly coupled wave and circulation model, SWAN + ADCIRC (i.e., Simulating WAves Nearshore + ADvanced CIRCulation), to hindcast the storm surge of Hurricane Rita (2005). Wind drag coefficient formulations of Powell, Zijlema, and Peng & Li are used to calculate wind stresses. Bottom friction and wind drag coefficients are systematically increased and decreased to quantify their impacts on the hindcast. Different meteorological forcing options are applied to study the effect of wind fields on storm surge development and propagation. Simulated water levels are compared with observed data collected from about 150 locations. It is evident that a lower bottom friction causes higher and faster surge propagation, and earlier arrival of inundation peak at locations far from the land fall. Drag coefficients of Powell, with or without a cap of 0.002, and Zijlema produce similar results, while that of Peng & Li slightly overpredicted the surge. Wind fields may cause overprediction or underprediction of the surge, depending on the choice of the wind model. A good agreement is found between Zijlema’s findings and this study; that simultaneously decreasing or increasing both bottom friction and wind drag essentially provides the same hindcast results.

Based on Finite Volume Coastal Ocean Model (FVCOM), this study constructed a numerical model covering the Bohai Sea, Yellow Sea, and East China Sea. National Centers for Environmental Prediction’s Climate Forecast System Reanalysis (NCEP-CFSR) data were used to drive the model to simulate a large storm surge generated by Typhoon Chan-hom. The model was validated by multiple in situ observations of water levels taken at tidal gauge stations. The effects of the nonlinear terms and topography on the modeling of storm surges were then studied. First, the tide–surge interaction during the storm surge process was analyzed. The results show that the tide–surge interaction can suppress the storm surge at the climax of the astronomical tide and benefit the growth of the storm surge at the ebb of the astronomical tide. The tidal constituents M2, S2 and K1 were added to analyze the influences of the amplitudes and periods of tides on the nonlinear reaction. The results indicate that the nonlinear effect will be enhanced by an increased tidal height; additionally, the nonlinear interaction of semidiurnal tides is more significant than that of diurnal tides. The semidiurnal fluctuation that appears near the peak-value time is also attributed to the tide–surge interaction. Moreover, the tide–surge interaction can be influenced by the water depth and position of the area of interest. According to the numerical results, topography has a certain impact on the storm surge: The peak value of the storm surge will decrease with increasing slope. The existence of the Ryukyu Islands reduces the area influenced by the storm surge on the southeast coast of China but expands the high-value storm surge area.

In this study, a storm surge model of the Salish Sea was developed to simulate storm surge in Puget Sound using the unstructured-grid, finite-volume community ocean model (FVCOM). The model was based on Pacific Northwest National Laboratory’s existing Puget Sound hydrodynamic model that has been validated extensively in previous studies. To identify representative historical storm events, a systematic approach was developed based on the non-tidal residual (NTR) method. A total of 34 storm surge events between 1980 and 2016 were identified and simulated. To accurately simulate storm surge inside Puget Sound, the model was driven by reanalyzed Climate Forecast System Reanalysis meteorological forcing and observed water levels at the entrance of Strait of Juan de Fuca and southern entrance of Johnstone Strait. Model results for total water level, storm surge, and currents were compared with field measurements. A series of error statistics parameters were also calculated to quantify the model’s skills in simulating storm surge in the Salish Sea. Error statistics of model performance metrics demonstrated that the Salish Sea storm surge model was able to simulate storm surge inside Puget Sound with high accuracy. Storm surge propagation into Puget Sound is a nonlinear process that cannot be simply determined by a static approach for the entire domain. The maximum NTR distribution inside Puget Sound suggests that storm surge tends to be amplified in several sub-basins and inlets of the Puget Sound, such as Bellingham Bay, Hood Canal, south Puget Sound, and the multi-inlet basin behind Agate and Rich Passages. Therefore, future study is necessary to further quantify the storm surge level in these high-risk areas through field measurements and refined model simulations.

Changes in fish harvest are not only impacted by fishing,but also influenced by climate variability.Based on data of hairtail catches between 1956 and 2006 in the Yellow Sea and Bohai Sea,China,the responses of catches to fishing and climate factors were analyzed.The catch time series can be compartmentalized into an in-ter-annual trend and variations.The inter-annual trend was mainly attributed to growth in fishing efforts.This was suggested by highly significant regression of hairtail catches to fishing effort fitted by the Fox model(P0.01).After the catch trend was removed,there were positive partial correlations(P0.02) of the residual variations in catches to winter wind speed in the Yellow Sea,summer wind speed in the East China Sea,and annual precipita-tion in Yellow River Valley and the coastal region of the Yellow Sea and Bohai Sea,whereas the partial correla-tions to sea surface temperature and summer wind speed in the Yellow Sea,as well as summer and winter wind speeds in the Bohai Sea were negative(P0.05).The linkage suggests that the climate factors govern the hairtail catch variations.It was inferred that runoff input nutrients to the Yellow Sea and Bohai Sea,while monsoons con-trolled the transport and distribution of the nutrients,and these processes can have impacts on the primary produc-tivity.Changes in water temperature could also directly affect the growth,feeding,spawning,and migration of the fish,and thus,influence the fish population size.The hairtail catch time series can be jointly fitted by fishing ef-fort and climatic factors(P0.01),suggesting that,in addition to fishing,climate factors can significantly influ-ence the hairtail catches.In the context of global warming,it was projected that the future climate changes would lead to reduction in hairtail production in the Yellow Sea and Bohai Sea,and the hairtail catch variations would become even greater.

TOPEX/Poseidon (T/P) can provide accurate tide data in the Yellow and Bohai Seas. The accuracy of T/P as a measurement tool for tides in the coastal seas can be evaluated through comparison to data from in situ pressure gauges and coastal tide stations. The effort here is concentrated on the broad continental shelf region of the Yellow and Bohai Seas. Tide coefficients are derived from five years of T/P data for M2, S2, K1, and O1 constituents. These constituents are responsible for about 80% of the tidal variability in the Yellow Sea. The T/P data are compared with two moored pressure gauges deployed along ground tracks and eight coastal tide gauges near the ground tracks. In addition, for internal consistency, the T/P data are compared at seven crossover points. Comparisons with tidal stations are quite good in the Yellow Sea, but not as good in the Bohai Sea. Comparisons of moored pressure gauges with T/P are better than coastal tide station comparisons in terms of tidal amplitude and phase. Crossover point comparisons are best in the Yellow Sea. The T/P data dropouts degrade the coefficient determination within 20 to 35 km of the coast.

New York City, New York (NYC), is extremely vulnerable to coastal flooding; thus, verification and improvements in storm surge models are needed in order to protect both life and property. This paper highlights the Stony Brook Storm Surge (SBSS) modeling system. It utilizes surface winds and sea level pressures from the fifth-generation Pennsylvania State University (PSU)-National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) or the Weather Research and Forecasting (WRF) model to drive the Advanced Circulation Model for Coastal Ocean Hydrodynamics (ADCIRC). For this study, the MM5 is utilized at 12-km grid spacing and ADCIRC is run on an unstructured grid down to ~10-m resolution in areas around Long Island and NYC. This paper describes the SBSS and its performance across the NYC region during the 11–12 December 1992 nor'easter and Tropical Storm Floyd on 16–17 September 1999. During the 1992 event, east-northeasterly surface winds of 15–25 m s−1 (30–50 kts) persisted for nearly 24 h, while hurricane-force winds (35–40 m s−1) occurred for a few hours just south of western Long Island. This created a 1.0–1.5-m storm surge around NYC and western Long Island Sound over three tidal cycles. ADCIRC successfully simulated the peak water levels to within ~10%, and it realistically simulated some of the flooding across lower Manhattan. The surface winds for Tropical Storm Floyd were only 5–10 m s−1 weaker than the 1992 event, but no coastal flooding occurred during Floyd, because the storm approached during a low tide. Additional Floyd simulations were completed by shifting the storm's landfall to the spring high tide the previous week, and by doubling the wind speed to mimic a category-1 hurricane. A combination of the spring high tide and a category-1 hurricane scenario during Floyd would have resulted in moderate flooding at several locations around NYC.

The floods associated with the effects of an incoming tropical cyclone have an immense effect in the Philippines, especially with respect to agriculture, industry, livelihood, and public safety. Knowledge of how such storm surge flooding can affect the community is therefore of great importance. In this study, the mechanisms behind Typhoon Haiyan’s anomalous storm surge flooding in 2013, which resulted in more than 6300 casualties and 2.86 billion USD worth of damage in the Philippines, were investigated. The Japan Meteorological Agency (JMA) storm surge model and the FLO-2D flood model were used to simulate Typhoon Haiyan’s storm surge height and the extent of inundation, respectively. The storm surge input data were obtained from JMA typhoon data, and the digital terrain models used were gathered from the airborne interferometric synthetic aperture radar data. The model’s accuracy was also validated using field validation data of the extent of the observed storm surge in affected coastal areas. Topographical analysis of the inundated regions showed the effects of coastal shape, elevation, and position relative to the typhoon’s approach angle on storm surge flow depth and velocity. Storm surge maximum velocity appears to increase as the fluid flows to an increasingly elevated area. Observing fluid velocity in a coastal area with uniform storm surge discharge from all directions also showed that flow velocity tends to increase at the center. Greater flood depths were experienced in areas with lower coastal elevation and not directly located at the coast, compared to higher elevation coastal areas. Greater extents of storm surge flooding are expected in coastal areas that have a concave shape, as fluid is more likely to be dispersed when hitting a convex coast. Extents are likewise observed to be greater in coastal regions that are located perpendicular to the direction of the typhoon. The research also validated the option of using a combination of typhoon and flood models to simulate the inundation flooding caused by extreme weather events.

Development of operational multi-scale storm surge inundated model and application of 2013 typhoon Haiyan

Unequivocal change in the climate system has put coastal regions around the world at increasing risk from climate-related hazards like storm surge flooding. With studies demonstrating an increase in the frequency of storm surges along the Northwest coast of Ireland, within the EU H2020 project SCORE (Smart Control of the Climate Resilience in European Coastal Cities) a numerical modelling approach has been undertaken using SHYFEM (System of HYdrodynamic Finite Element Model) to simulate the storm surges into 2100 for the NW of Ireland under different Climate scenarios.   Using the significant grid flexibility offered by the finite elements within SHYFEM, unstructured grids are implemented for two computational domains. One confined to the region of interest, specifically the Donegal Bay, namely the Limited Area Model (LAM) and the other extended to span the entirety of the North Atlantic Basin namely the Basin Scale Model (BSM). The two model applications are designed to capture the development of storm surges due to wind shear stresses and barometric pressure fluctuations associated with the passage of storm systems across the North Atlantic and to quantify the influence of the tides on the tide surge interaction. Given the large tidal excursion in the region, baroclinicity is neglected and the models are implemented in the barotropic mode. The LAM is forced with surface atmospheric forcing (pressure and winds) from ECMWF and sea surface height (SSH, including eleven tidal components) from CMEMS at its open boundary, whereas the BSM is solely forced with the atmospheric forcing. Extensive calibration of wind drag and bottom friction coefficients for the LAM and BSM using tidal gauge observations for 2020, shows high P. Corr and acceptable RMSE. Only negligible differences, around 3 cm, are seen in the surge magnitudes between the LAM and the BSM implying the negligible non-linear interaction of the tides on the storm surge dynamics in the Donegal Bay.  Thus, the BSM has been forced with atmospheric forcing obtained from CMIP6 climate models to predict the impact of climate changes on storm surge intensity and frequency under different climate scenarios up to 2100. Finally, by accounting for the tidal contribution, total water levels projections have been determined thus facilitating the generation of useful statistics (for instance Annual exceedance probabilities) and flood maps to inform relevant policies for coastal resilience. 

Consumption of pesticides in China has increased rapidly in recent years; however, occurrence and fate of current‐use pesticides (CUPs) in China coastal waters are poorly understood. Globally banned pesticides, so‐called historic‐use pesticides (HUPs), are still commonly observed in the environment. In this work, air and surface seawater samples taken from the Bohai and Yellow Seas in May 2012 were analyzed for CUPs including trifluralin, quintozene, chlorothalonil, dicofol, chlorpyrifos, and dacthal, as well as HUPs (hexachlorobenzene (HCB), hexachlorocyclohexanes (HCHs), and endosulfan). CUP profile in both air and seawater samples generally reflected their consumption patterns in China. HUPs in the air and seawater samples were in comparable levels as those of CUPs with high concentrations. α‐Endosulfan, dicofol, and chlorothalonil showed strong net deposition likely resulting from their intensive use in recent years, while CUPs with low consumption amount (quintozene and dacthal) were close to equilibrium at most samplings sites. Another CUP with high usage amout (i.e., chlorpyrifos) underwent volatilization possibly due to its longer half‐life in seawater than that in air. α‐HCH and γ‐HCH were close to equilibrium in the Bohai Sea, but mainly underwent net deposition in the Yellow Sea. The net deposition of α‐HCH could be attributed to polluted air pulses from the East China identified by air mass back trajectories. β‐HCH showed net volatilization in the Bohai Sea, which was driven by its relative enrichment in seawater. HCB either slightly favored net volatilization or was close to equilibrium in the Bohai and Yellow Seas.

Storm surge risk assessment for the insurance system: A case study in Tokyo Bay, Japan

Probability forecast of storm surge levels in the Changjiang Estuary induced by tropical cyclones based on the Error-Estimation Ensemble method

Heavy precipitation and storm surges often co-occur and compound together to form sudden and severe flooding events. However, we lack comprehensive observational tools with high temporal and spatial resolution to capture these fast-evolving hazards. Remotely sensed images provide extensive spatial coverage, but they may be limited by adverse weather conditions or platform revisiting schedule. River gauges could provide frequent water height measurement but they are sparsely distributed. Riverine flood and storm surge models, depending on input data quality and calibration process, have various uncertainties. These lead to inevitable temporal and spatial gaps in monitoring inundation dynamics. To fill in the observation gaps, this paper proposes a probabilistic method to estimate daily inundation probability by combining the information from multiple sources, including satellite remote sensing, riverine flood depth, storm surge height, and land cover. Each data source is regarded as a spatial evidence layer, and the weight of evidence is calculated by assessing the association between the evidence presence and inundation occurrence. Within a Bayesian model, the fusion results are daily inundation probability whenever at least one data source is available. The proposed method is applied to estimate daily inundation in Harris, Texas, impacted by Hurricane Harvey. The results agree with the reference water extent, high water mark, and extracted tweet locations. This method could help to further understand flooding as an evolving time-space process and support response and mitigation decisions.