Extreme Storm Tide Research Articles

Early Detection of Extreme Storm Tide Events Using Multimodal Data Processing

Sea-level rise is a well-known consequence of climate change. Several studies have estimated the social and economic impact of the increase in extreme flooding. An efficient way to mitigate its consequences is the development of a flood alert and prediction system, based on high-resolution numerical models and robust sensing networks. However, current models use various simplifying assumptions that compromise accuracy to ensure solvability within a reasonable timeframe, hindering more regular and cost-effective forecasts for various locations along the shoreline. To address these issues, this work proposes a hybrid model for multimodal data processing that combines physics-based numerical simulations, data obtained from a network of sensors, and satellite images to provide refined wave and sea-surface height forecasts, with real results obtained in a critical location within the Port of Santos (the largest port in Latin America). Our approach exhibits faster convergence than data-driven models while achieving more accurate predictions. Moreover, the model handles irregularly sampled time series and missing data without the need for complex preprocessing mechanisms or data imputation while keeping low computational costs through a combination of time encoding, recurrent and graph neural networks. Enabling raw sensor data to be easily combined with existing physics-based models opens up new possibilities for accurate extreme storm tide events forecast systems that enhance community safety and aid policymakers in their decision-making processes.

Investigation of historical severe storms and storm tides in the German Bight with century reanalysis data

Abstract. Century reanalysis models offer a possibility to investigate extreme events and gain further insights into their impact through numerical experiments. This paper is a comprehensive summary of historical hazardous storm tides in the German Bight (southern North Sea) with the aim of comparing and evaluating the potential of different century reanalysis data to be used for the reconstruction of extreme water levels. The composite analysis of historical water level extremes, underlying atmospheric situations and their uncertainties may further support decision-making on coastal protection and risk assessment. The analysis is done based on the results of the regional hydrodynamic model simulations forced by atmospheric century reanalysis data, e.g. 20th Century Reanalysis Project (20CR) ensembles, ERA5 and UERRA–HARMONIE. The eight selected historical storms lead either to the highest storm tide extremes for at least one of three locations around the German Bight or to extreme storm surge events during low tide. In general, extreme storm tides could be reproduced, and some individual ensemble members are suitable for the reconstruction of respective storm tides. However, the highest observed water level in the German Bight could not be simulated with any considered forcing. The particular weather situations with corresponding storm tracks are analysed to better understand their different impact on the peak storm tides, their variability and their predictability. Storms with more northerly tracks generally show less variability in wind speed and a better agreement with the observed extreme water levels for the German Bight. The impact of two severe historical storms that peaked at low tide is investigated with shifted tides. For Husum in the eastern German Bight this results in a substantial increase in the peak water levels reaching a historical maximum.

Enabling dynamic modelling of coastal flooding by defining storm tide hydrographs

Abstract. Coastal flooding is driven by the combination of (high) tide and storm surge, the latter being caused by strong winds and low pressure in tropical and extratropical cyclones. The combination of storm surge and the astronomical tide is defined as the storm tide. To gain an understanding of the threat posed by coastal flooding and to identify areas that are especially at risk, now and in the future, it is crucial to accurately model coastal inundation. Most models used to simulate the coastal inundation scale follow a simple planar approach, referred to as bathtub models. The main limitations of this type of models are that they implicitly assume an infinite flood duration, and they do not capture relevant physical processes. In this study we develop a method to generate hydrographs called HGRAPHER, and we provide a global dataset of storm tide hydrographs based on time series of storm surges and tides derived from the Global Tide and Surge Model (GTSM) forced with the ERA5 reanalysis wind and pressure fields. These hydrographs represent the typical shape of an extreme storm tide at a certain location along the global coastline. We test the sensitivity of the HGRAPHER method with respect to two main assumptions that determine the shape of the hydrograph, namely the surge event sampling threshold and coincidence in the time of the surge and tide maxima. The hydrograph dataset can be used to move away from planar inundation modelling techniques towards dynamic inundation modelling techniques across different spatial scales.

Determination of Current and Future Extreme Sea Levels at the Local Scale in Port-Bouët Bay (Côte d’Ivoire)

The Port-Bouët Bay shoreline is threatened by extreme sea level (ESL) events, which result from the combination of storm tide, wave run-up, and sea level rise (SLR). This study provides comprehensive scenarios of current and future ESLs at the local scale along the bay to understand the evolution of the phenomenon and promote local adaptation. The methodological steps involve first reconstructing historical storm tide and wave run-up data using a hydrodynamic model (D-flow FM) and the empirical model of Stockdon et al. Second, the Generalized Pareto Distribution (GPD) model fitted to the Peaks-Over-Thresholds (POT) method is applied to the data to calculate extreme return levels. Third, we combine the extreme storm tide and wave run-up using the joint probability method to obtain the current ESLs. Finally, the current ESLs are integrated with recent SLR projections to provide future ESL estimates. The results show that the current ESLs are relatively high, with 100-year return levels of 4.37 m ± 0.51, 4.97 m ± 0.57, and 4.48 m ± 0.5 at Vridi, Petit-Bassam, and Sogefiha respectively. By end-century, under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, the future SLR is expected to increase the current ESLs by 0.49 m, 0.62 m, and 0.84 m, respectively. This could lead to a more frequent occurrence of the current 100-year return period, happening once every 2 years by 2100, especially under SSP5-8.5. The developed SLR scenarios can be used to assess the potential coastal flood risk in the study area for sustainable and effective coastal management and planning.

Forecasting of Extreme Storm Tide Events Using NARX Neural Network-Based Models

The extreme values of high tides are generally caused by a combination of astronomical and meteorological causes, as well as by the conformation of the sea basin. One place where the extreme values of the tide have a considerable practical interest is the city of Venice. The MOSE (MOdulo Sperimentale Elettromeccanico) system was created to protect Venice from flooding caused by the highest tides. Proper operation of the protection system requires an adequate forecast model of the highest tides, which is able to provide reliable forecasts even some days in advance. Nonlinear Autoregressive Exogenous (NARX) neural networks are particularly effective in predicting time series of hydrological quantities. In this work, the effectiveness of two distinct NARX-based models was demonstrated in predicting the extreme values of high tides in Venice. The first model requires as input values the astronomical tide, barometric pressure, wind speed, and direction, as well as previously observed sea level values. The second model instead takes, as input values, the astronomical tide and the previously observed sea level values, which implicitly take into account the weather conditions. Both models proved capable of predicting the extreme values of high tides with great accuracy, even greater than that of the models currently used.

Extreme storm tides in the German Bight (North Sea) and their potential for amplification

Abstract. Storm tides are a major hazard for the German North Sea coasts. For coastal protection and economic activities, planning information on the probability and magnitude of extreme storm tides and their possible future changes is important. This study focuses on the most extreme events and examines whether they could have become more severe under slightly different conditions while still remaining within physical plausibility. In the face of a limited number of observational data on very severe events, an extensive set of model data is used to extract most extreme storm tide events for locations in the German Bight, in particular Borkum and the Ems estuary. The data set includes water levels and respective atmospheric conditions from a hindcast and future climate realizations without sea level rise describing today's and possible future conditions. A number of very severe events with water levels exceeding those measured near Borkum since 1906 are identified in the data set. A possible further amplification of the highest events is investigated by simulating these events for the North Sea with different phase lags between the astronomical tide given at the open model boundaries and the wind forcing. It is found that superposition of spring tide conditions, different timing of the astronomical high water and atmospheric conditions during the highest storm event would cause an enhancement of the highest water level up to about 50 cm. The water levels of the two highest events from the data set are used to analyse the effects in the Ems estuary using a high-resolution model of the German Bight. Additionally, the influences of an extreme river runoff and of sea level rise are studied. The extreme river runoff of 1200 m3 s−1 increases the highest water levels by several decimetres in the narrow upstream part of the Ems estuary. This effect diminishes downstream. The sea level rise increases the water level in the downstream part of the Ems estuary by the amount applied at the model boundary to the North Sea. In the upstream part, its influence on the water level decreases. This study may serve as a first step towards an impact assessment for severe storm tides and towards implications for coastal zone management in times of climate change.

Spatial and temporal analysis of extreme storm-tide and skew-surge events around the coastline of New Zealand

Abstract. Coastal flooding is a major global hazard, yet few studies have examined the spatial and temporal characteristics of extreme sea level and associated coastal flooding. Here we analyse sea-level records around the coast of New Zealand (NZ) to quantify extreme storm-tide and skew-surge frequency and magnitude. We identify the relative magnitude of sea-level components contributing to 85 extreme sea level and 135 extreme skew-surge events recorded in NZ since 1900. We then examine the spatial and temporal clustering of these extreme storm-tide and skew-surge events and identify typical storm tracks and weather types associated with the spatial clusters of extreme events. We find that most extreme storm tides were driven by moderate skew surges combined with high perigean spring tides. The spring–neap tidal cycle, coupled with a moderate surge climatology, prevents successive extreme storm-tide events from happening within 4–10 d of each other, and generally there are at least 10 d between extreme storm-tide events. This is similar to findings from the UK (Haigh et al., 2016), despite NZ having smaller tides. Extreme events more commonly impacted the east coast of the North Island of NZ during blocking weather types, and the South Island and west coast of the North Island during trough weather types. The seasonal distribution of both extreme storm-tide and skew-surge events closely follows the seasonal pattern of mean sea-level anomaly (MSLA) – MSLA was positive in 92 % of all extreme storm-tide events and in 88 % of all extreme skew-surge events. The strong influence of low-amplitude (−0.06 to 0.28 m) MSLA on the timing of extreme events shows that mean sea-level rise (SLR) of similarly small height will drive rapid increases in the frequency of presently rare extreme sea levels. These findings have important implications for flood management, emergency response and the insurance sector, because impacts and losses may be correlated in space and time.

Identification of extreme storm surges with high-impact potential along the German North Sea coastline

Planning and design of coastal protection rely on information about the probabilities of very severe storm tides and the possible changes that may occur in the course of climate change. So far, this information is mostly provided in the form of high percentiles obtained from frequency distributions or return values. More detailed information and assessments of events that may cause extreme damages or have extreme consequences at the coast are so far still unavailable. We describe and compare two different approaches that may be used to identify highly unlikely but still physically possible and plausible events from model simulations. Firstly, in the case when consistent wind and tide-surge data are available, different metrics such as the height of the storm surge can be derived directly from the simulated water levels. Secondly, in cases where only atmospheric data are available, the so called effective wind may be used. The latter is the projection of the horizontal wind vector on that direction which is most effective in producing surges at the coast. Comparison of events identified by both methods show that they can identify extreme events but that knowledge of the effective wind alone does not provide sufficient information to identify the highest storm surges. Tracks of the low-pressure systems over the North Sea need to be investigated to find those cases, where the duration of the high wind is too short to induce extreme storm tides. On the other hand, factors such as external surges or variability in mean sea level may enhance surge heights and are not accounted for in estimates based on effective winds only. Results from the analysis of an extended data set suggest that unprecedented storm surges at the German North Sea coast are possible even without taking effects from rising mean sea level into account. The work presented is part of the ongoing project “Extreme North Sea Storm Surges and Their Consequences” (EXTREMENESS) and represents the first step towards an impact assessment for very severe storm surges which will serve as a basis for development of adaptation options and evaluation criteria.

Distribution Characteristics of the Extreme Storm Tides in the Radial Sand Ridges Area of the South Yellow Sea in China

Xie, D.H.; Tan, Y.; Chu, A.; Zhou, T.Y., and van Maanen, B., 2018. Distribution characteristics of the extreme storm tides in the radial sand ridges area of the South Yellow Sea in China In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 856–860. Coconut Creek (Florida), ISSN 0749-0208.The aim of this study is to understand the tidal characteristics in the radial sand ridges area (South Yellow Sea, China) considering the most adverse metrological and hydrodynamic condition. Based on data analysis, at least 30% of the annual highest tidal levels in the South Yellow Sea are caused by the combination of storm surge and astronomical spring tides. Among the top 10 extreme high tidal levels, such combinational effects account for over 60%. A 2-D numerical model is applied to study distributions of the extreme high tidal levels influenced by typhoons with various tracks in the South Yellow Sea. Typhoon characteristics are classified into four categories: the front landing, the front leaving, the offshore moving and other types. As such, four different combinations with the same astronomical tide are examined. The front landing and the offshore moving tracks are of specific interest, as they lead to the most significant surge in coastal areas, especially at Jianggang and Liyashan. Temporal-spatial patterns of the extreme storm tides are presented to identify risks of coastal flooding in the radial sand ridges area.

Development of an adaptive Kalman filter-based storm tide forecasting model

Extreme storm tide usually causes flooding of low-lying land in a coastal city. Hence, developing an efficient and accurate forecasting model for issuing a timely warning is important. In this study, an adaptive Kalman filter-based storm tide forecasting model was proposed and applied to the inner Harbor of Macau. The model is a dynamic linear regression model with the harmonic tidal prediction, wind speed, wind direction and atmospheric pressure as its input parameters. With persistence forecast of weather assumed during the prediction period, the model was tested with 40 cases of storm tide induced by tropical cyclones in Macau between 2005 and 2012. Success was found for forecasts with lead times up to 3 hours. The proposed adaptive model is considered a practical tool for storm tide forecast in small coastal cities.

Application of a synthetic cyclone method for assessment of tropical cyclone storm tides in Samoa

Tropical cyclone-induced storm surges cause damaging impacts in coastal regions. The present study uses a stochastic cyclone modelling approach to evaluate the likelihoods of storm tides, the combination of storm surges and astronomical tides, for Samoa. Cyclones that occurred in the vicinity of Samoa from 1969 to 2009 are used to build a stochastic tropical cyclone data set, and an analytic cyclone model and hydrodynamic model are used to model storm tides under average, La Nina and El Nino cyclone and sea level conditions for present climate conditions as well as cyclone and sea level conditions relevant for 2055, and storm tide return periods are estimated. We find that extreme storm tides exhibit relatively modest variation around the coastline of Samoa owing to the uniform width of the shelf surrounding the coastlines of two main islands of Savai’i and Upolu. The frequency of cyclones and hence storm tides during El Nino conditions is similar to the frequency for all seasons, but is considerably lower in La Nina conditions. For the future, tropical cyclones are assumed to undergo decreased frequency and increased intensity. This is found to lower the storm tide height for return periods <100 years and increase it for return periods greater than about 200 years. Sea level rise is shown to have a larger influence on storm tides than future changes to tropical cyclones. Considering the aggregated probabilities of storm tides occurring at the national scale, we find that the likelihood of a storm tide occurring that locally exceeds a 1-in-100-year level (i.e. an event with a 1 % annual exceedance probability) has a 6 % probability of occurring somewhere along the entire coastline of Samoa. Such information may be useful for those involved in coastal management and disaster response for which there may be a need to consider the overall likelihood that a nation may have to respond to such a disaster.

A Hybrid Approach Using Hydrodynamic Modeling and Artificial Neural Networks for Extreme Storm Surge Prediction

On coastlines with shallow shelf areas (e.g. North Sea), a combination of high tides, storm surges, wind waves and mutual interactions generally represent the major sources of coastal flood risks: The contribution of the mutual interactions between the various components still remains the least understood, despite advancements in the current operational storm-tide models. The greatest difficulties arise from the fact that most of these nonlinear interactions are physically unknown and that the entire storm-tide system is highly complex and of stochastic nature. A pragmatic data-driven approach, which can use artificial neural networks (ANNs), is required to assess the contributions of these nonlinear interactions to the resulting extreme storm-tide. Such a pragmatic approach is proposed, which is based on two types of extreme water level ANNs models called nonlinear autoregressive exogenous inputs (NARX): (i) NARX neural network model to predict the extreme storm-tide (Type-A), (ii) NARX neural network model to nonlinearly correct the numerical storm- tide results from TELEMAC2D and TOMAWAC (Type-B). Ensembles methods are then used to reduce variance and minimize error especially in extreme storm-tide events. Several ensemble fitting neural (EFN) network models are developed and tested. The approach was applied for two pilot sites in the North Sea (Cuxhaven and Sylt). The results show that the ensemble models are able to extract the contribution of the nonlinear interaction between the different extreme storm-tide components at both sites by subtracting the results of the hydrodynamic models (linear superposition of storm-tide components) from the ensemble results. In most extreme storm-tide events considered in this study, the contribution of the nonlinear interaction resulted in the reduction of the extreme water levels when compared with the linear superposition of extreme storm-tide components. However, under certain conditions, the nonlinear interactions might result in higher storm-tides than the linear superposition (e.g. storm of January 2000 at Cuxhaven and Sylt).

The use of copula functions for predictive analysis of correlations between extreme storm tides

In this paper we present a method used in quantitative description of weakly predictable hydrological, extreme events at inland sea. Investigations for correlations between variations of individual measuring points, employing combined statistical methods, were carried out. As a main tool for this analysis we used a two-dimensional copula function sensitive for correlated extreme effects. Additionally, a new proposed methodology, based on Detrended Fluctuations Analysis (DFA) and Anomalous Diffusion (AD), was used for the prediction of negative and positive auto-correlations and associated optimum choice of copula functions. As a practical example we analysed maximum storm tides data recorded at five spatially separated places at the Baltic Sea. For the analysis we used Gumbel, Clayton, and Frank copula functions and introduced the reversed Clayton copula. The application of our research model is associated with modelling the risk of high storm tides and possible storm flooding.

Understanding the variability of an extreme storm tide along a coastline

Correctly determining the peak storm tide height along the coastline, and expressing the associated natural variability, is essential for a robust prediction of coastal flood risk. A new approach is proposed that calculates a storm tide relationship (relative to a tide gauge) by using a storm surge model to describe the natural spatial variability based on the features of a large number of very high storm tides. Two historic flood events (1953 and 2007) were used to validate this characteristics approach along the East Anglia coastline (U.K.), and predicted water-levels were found to be in good agreement with tide gauge observations (Root Mean Squared Error of 36 cm), especially when compared to the method of assuming a storm tide of constant return period (Root Mean Squared Error of 59 cm). Detailed observations of storm tide height between tide gauge locations are rare; therefore, Synthetic Aperture Radar (SAR) was employed to calculate the LiDAR geo-referenced storm tide height along the North Somerset coastline of the Bristol Channel (U.K.). Two SAR observed “extreme” storm tide events were used to validate the characteristics approach between tide gauges (Root Mean Squared Error of 1.2 m and 0.7 m), and indicated the presence of localised wave effects to the observed storm tide height that could have a significant effect to flood risk estimates.

Near-Field Survey of the 1946 Aleutian Tsunami on Unimak and Sanak Islands

The 1946 Aleutian earthquake stands out among tsunamigenic events because it generated both very high run-up near the earthquake source region and a destructive trans-Pacific tsunami. We obtained new data on the distribution of its tsunami in the near field along south-facing coasts between Unimak Pass on the west and Sanak Island on the east by measuring the height of driftwood and beach materials that were deposited by the tsunami above the extreme storm tide level. Our data indicate that (1) the highest measured run-up, which is at the Scotch Cap light-house, was 42 m above tide level or about 37 m above present storm tide elevation; (2) run-up along the rugged coast from Scotch Cap for 12 km northwest to Sennett Point is 12–18 m, and for 30 km east of Scotch Cap to Cape Lutke it is 24–42 m; (3) run-up along the broad lowlands bordering Unimak Bight is 10–20 m, and inundation is locally more than 2 km; (5) run-up diminishes to 8 m or less at the southeast corner of Unimak Island; (6) no evidence was found for run-up above present storm tides (about 4–5 m above MLLW) on the Ikatan Peninsula or areas along the coast to the west; and (7) run-up above storm tide level in the Sanak Island group is restricted to southwest-facing coasts of Sanak, Long, and Clifford Islands, where it is continuous and locally up to 24 m high. Generation of the tsunami by one or more major earthquake-triggered submarine landslides near the shelf edge south of Unimak Island seems to be the only viable mechanism to account for the data on wave arrival time, run-up heights, and distribution, as well as for unconfirmed anecdotal reports of local postquake increases in water depth and diminished bottom-fisheries productivity. A preliminary hydrodynamic simulation of the local tsunami propagation and run-up using a dipolar model of a possible landslide off Davidson Bank provides an acceptable fit to the characteristics of the distribution of local run-up, with a value at 34 m at the Scotch Cap lighthouse.

Coastal cities: living on the edge.

Coastal cities: living on the edge.