New Statistical Approach to Select Coastal Flood-Producing Extratropical Cyclones from a 10,000-Year Stochastic Catalog

Storm surge in the Chengshantou sea area is simulated by the advanced circulation (ADCIRC) model. The Regional Atmospheric Modeling System (RAMS) wind model is used to simulate wind parameters used to be wind force of storm surge model. The comparison results of storm surges show good agreement between simulations and observations. The storm surge is caused by typhoon in summer but extratropical cyclone in winter. By statistical analysis of storm surge in 20 years, it is found that the annual max negative storm surge are all caused by extratropical cyclone while the frequency of annual max positive storm surge caused by tropical cyclone is about 20%. The extreme parameters are estimated by Gumbel distribution. The positive extreme storm surge of 100 year return period is determined as 95 cm and the negative is -124 cm.

Comparison between the skew surge and residual water level along the coastline of China

<p>Storm surges are driven by low air pressure and strong winds in tropical (TC) and extratropical cyclones (ETC). Coastal flooding is often caused by this type of extreme weather with large socio-economic impacts in densely populated and low-lying coastal areas. Recent examples of coastal disasters include typhoon Hagibis that made landfall in Japan, Hurricane Dorian which devastated the northwestern Bahamas, and extratropical cyclone Xaver that affected northern Europe. Each of these storms generated dangerous storm surges, reaching 6m in some parts of the Bahamas during Hurricane Dorian with approximately 100 fatalities as a result. Economic losses are estimated at 10 billion U.S. Dollars for both typhoon Hagibis and hurricane Dorian.</p><p>To inform flood risk management and develop effective adaptation strategies it is important to have accurate information on return periods of extreme sea levels. To date, there exists no global database with return periods of extreme sea levels that fully includes TCs. Global databases of extreme sea levels are typically based on historical climate simulations covering multiple decades. While this is sufficient for ETCs, TCs will be underestimated in such databases. This because TCs have generally low probabilities and affect only a small stretch of coastline, compared to ETCs. A climate reanalysis covering multiple decades includes too few TCs to perform an extreme value analysis. To resolve this, previous studies at local scale have used synthetic TC tracks generated by a statistical model to estimate the probabilities of extreme sea levels.</p><p>The aim of this research is to develop a global database of extreme sea levels that include both ETCs and TCs. For ETCs, we force the hydrodynamic Global Tide and Surge Model (GTSM) with ERA5 10-meter wind speed and air pressure data to calculate the return periods of extreme sea levels based on the period 1979-2017. Since ERA5 includes all storms, we filter out extreme sea levels caused by TCs. Preliminary results show that GTSM forced with ERA5 atmospheric data performs well for ETCs. For TCs, we force GTSM with synthetic TC tracks that correspond to 10.000 year of TC statistics. The synthetic tracks of TCs are obtained from the STORM model (Bloemendaal et al., in review) based on the International Best Track Archive for Climate Stewardship (IBTrACS) TC database. With STORM it is possible to statistically extend the ~38-year observed dataset to a 10.000-year synthetic dataset. The synthetic dataset preserves the climatological statistics as found in the original dataset. Finally, we will merge the TC and ETC related return periods to create a global extreme sea level database.</p>

Storm surges can be described as nonperiodic fluctuations in sea levels associated with variations in wind stress and atmospheric pressure, caused by the approach of cyclones or extratropical storms. They can have catastrophic results on coastal communities, particularly in combination with high tides and large waves. Therefore, for the efficiency of storm surge modeling, the accuracy and resolution of meteorological data as well as hydrodynamic processes, which are essentially governed by the atmospheric flow under such strong wind conditions, are particularly crucial. In addition to the accurate forecasts of wind speed, there is uncertainty in translating wind speed to the wind shear stress, which is an essential contributor to storm surges that quantify the driving force of wind to ocean flow using a sea-surface drag in the numerical models.The main purpose of this study is to improve our understanding of the role of sea surface drag parametrization in storm surge dynamics and reproduction within the Global Tide and Surge Model (GTSM). The depth-averaged hydrodynamic model GTSM, which has global coverage and spatially varying resolution increasing towards the coast, can be used to simulate changes in water levels and currents caused by tides and storm surges. In this study, the GTSM is used to compute tide and surge combined to provide accurate tide-surge interactions, and we conduct a sensitivity study of the storm surges to evaluate the performance of various sea surface drag parameterization in predicting storm surge behavior to determine the most suitable one. The model's performance is assessed based on a comparison of modeled and observed storm surges, both being estimated based on tidal analysis of total water levels. We investigate the performance from 2006 to 2022 and analyze 20 specific extreme weather events, such as extra-tropical storm Xaver that occurred over the North Sea and post-tropical cyclone Fiona hit Canada. The results of this study will provide valuable insights into the most suitable sea surface drag parameterization for the prediction of tide-surge interactions, surge signal's mean behavior, and storm surge dynamics within the GTSM under storm conditions.

Nadal-Caraballo, N.C.; Campbell, M.O.; Gonzalez, V.M.; Torres, M.J.; Melby, J.A., and Taflanidis, A.A., 2020. Coastal Hazards System: A Probabilistic Coastal Hazard Analysis Framework. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1211-1216. Coconut Creek (Florida), ISSN 0749-0208.Coastal hazards such as storm surge, waves, currents, wind, and rainfall associated with extratropical and tropical cyclones (e.g., hurricanes) can have devastating effects and threaten the lives of millions of people living along the world's coastlines. This has led to the development of the Coastal Hazards System (CHS), an ambitious program with the primary goal of quantifying extreme coastal hazards due to both tropical and extratropical cyclones. The CHS currently covers all U.S. hurricane-prone coastlines along the Gulf of Mexico, the Atlantic seaboard, Puerto Rico, and the U.S. Virgin Islands, as well as regions affected only by extratropical storms like the Great Lakes. The Probabilistic Coastal Hazard Analysis (PCHA) is an innovative statistical and probabilistic framework that serves as the foundation of the CHS. This framework includes comprehensive characterization of storm climatology, high-resolution numerical modeling, and advanced joint probability analysis of atmospheric forcing and primary storm responses, including associated aleatory and epistemic uncertainties. New PCHA advancements overcome limitations of previous joint probability methods by integrating meta-Gaussian copula for the computation of storm probabilities, spatially-varying bias and uncertainty quantification, and Gaussian process metamodel (GPM) based on existing CHS synthetic tropical cyclones. GPM emulates the response of computationally-expensive hydrodynamic models and enables the generation of augmented tropical cyclone suites, consisting of up to millions of storms, for optimal coverage of the parameter and probability spaces. The CHS also includes a database and a web-based data mining and visualization system for public distribution of high-fidelity probabilistic, atmospheric and hydrodynamic modeling results. PCHA results are distributed along with the corresponding metadata, in self-describing data formats for easy and efficient use by engineers, planners, economists, emergency managers, and researchers.

Abstract. A common feature within coastal cities is small, urbanized watersheds where the time of concentration is short, leading to vulnerability to flash flooding during coastal storms that can also cause storm surge. While many recent studies have provided evidence of dependency in these two flood drivers for many coastal areas worldwide, few studies have investigated their co-occurrence locally in detail or the storm types that are involved. Here we present a bivariate statistical analysis framework with historical rainfall and storm surge and tropical cyclone (TC) and extratropical cyclone (ETC) track data, using New York City (NYC) as a mid-latitude demonstration site where these storm types play different roles. In contrast to prior studies that focused on daily or longer durations of rain, we apply hourly data and study simultaneous drivers and lags between them. We quantify characteristics of compound flood drivers, including their dependency, magnitude, lag time, and joint return periods (JRPs), separately for TCs, ETCs, non-cyclone-associated events, and merged data from all events. We find TCs have markedly different driver characteristics from other storm types and dominate the joint probabilities of the most extreme rain surge compound events, even though they occur much less frequently. ETCs are the predominant source of more frequent moderate compound events. The hourly data also reveal subtle but important spatial differences in lag times between the joint flood drivers. For Manhattan and southern shores of NYC during top-ranked TC rain events, rain intensity has a strong negative correlation with lag time to peak surge, promoting pluvial–coastal compound flooding. However, for the Bronx River in northern NYC, fluvial–coastal compounding is favored due to a 2–6 h lag from the time of peak rain to peak surge.

This study explored the effects of interactions between waves and current on storm surge in the Pearl River Estuary (PRE) using a fully coupled wave–current model. The model was validated based on in situ observations during the traverse of super typhoon Mangkhut. The results indicated that the model could reproduce the storm surge and wave setup processes. Numerical experiments showed that simulations of storm surge are minimally affected by wave setup. The wave setup during super typhoon Mangkhut reached up to 0.23 m and contributed to the total near shore storm surge by up to 8%. The simulations of the coupled model showed a better correlation with observations compared to those of an uncoupled model. The storm surge increased with transport upstream in a tidal-dominated outlet, whereas it decreased in a river-dominated outlet. The storm surge and wave setup increased and decreased, respectively, during spring tide as compared to that during a neap tide. The storm surge increased with increasing runoff in the upper river reaches, whereas there was little change in the tidal-dominated lower river reaches. This research emphasizes the importance of integrating the effects of multiple dynamic factors in the forecasting of storm surge and provides a reference for similar studies in other estuaries with multiple outlets and a complex river network.

Storm surges along the German North Sea coast are tidal surges that reach a peak of 1.5 metres or more above mean high water (MHW). Severe and very severe storm surges exceed 2.5 and 3.5 metres above mean high water respectively. Storm surges along the German North Sea coast are triggered by westerly winds from approx. 7 to 8 Beaufort. At the Hamburg, St. Pauli gauge, the long-term average is 5 to 6 storm surges per year; less frequent are severe (2 in 3 years) and very severe storm surges (every 5 years). The period of occurrence is essentially limited to the winter half-year.The Federal Maritime and Hydrographic Agency (BSH) is responsible for warning of storm surges along the Baltic and North Sea coasts and the tidal river sections of the Ems, Weser, Jade and Elbe. Warnings of storm surges are distributed in very different ways. Warnings have been broadcast on the radio for many decades. In Hamburg, firecrackers are set off by the police. In recent years, dissemination via the BSH website and a customisable telephone distribution system has become established. Since the 2021/22 storm surge season, warnings have been fed into warning apps such as NINA via the Modular Warning System (MoWaS) of the Federal Office of Civil Protection and Disaster Assistance (BBK) and thus reach more directly affected citizens.The possibilities offered by new media make it necessary to further develop warning strategies. For example, we are currently working closely with the BBK on the automated provision of warnings via the NINA warning app. This leads to faster and more precise distribution of storm surge forecasts.The warnings and forecasts described take place against the background of the mean sea level rise and the associated rise in mean high water level. It is also the responsibility of the Federal Maritime and Hydrographic Agency to monitor this and to provide and analyse it at tide gauges such as Cuxhaven Steubenhöft, where measurements have been taken for around 180 years.When creating the forecasts, we set up the Flood Early Warning System (FEWS), which pools and helps to process the data and creates and publishes reports. With the help of our developed Model Output Statistics System (BSH-MOS), precise and individualised forecasts up to one week into the future are possible for up to 40 gauges. Among other things, MOS evaluates water level measurements, wind forecasts from the German Weather Service (DWD) and area-based modelled water level forecasts from the BSH model system.

There is increasing interest in the use of nature-based approaches for mitigation of storm surges along coasts, deltas, and estuaries. However, very few studies have quantified the effectiveness of storm surge height reduction by a real-existing, estuarine-scale, nature-based, and engineered flood defense system, under specific storm surge conditions. Here, we present data and modelling results from a specific storm surge in the Scheldt estuary (Belgium), where a hybrid flood defense system is implemented, consisting of flood control areas, of which some are restored into tidal marsh ecosystems, by use of culvert constructions that allow daily reduced tidal in- and outflow. We present a hindcast simulation of the storm surge of 6 December 2013, using a TELEMAC-3D model of the Scheldt estuary, and model scenarios showing that the hybrid flood defense system resulted in a storm surge height reduction of up to half a meter in the estuary. An important aspect of the work was the implementation of model formulations for calculating flow through culverts of restored marshes. The latter was validated comparing simulated and measured discharges through a physical scale model of a culvert, and through a real-scale culvert of an existing restored marsh during the storm surge.

Data assimilation in the continental shelf model

The Hurricane Research Division (HRD) analyzes surface wind fields in tropical storms and hurricanes using surface wind observations and aircraft flight-level wind measurements in the vicinity of the storms. The analyzed surface wind fields for Tropical Storm Marco (1990) were compared with the wind fields used for input in the National Weather Service's Sea, Lake, and Overland Surge from Hurricanes (SLOSH) model. The HRD wind fields were also used to determine the wind speeds and directions corresponding to the storm surge at tide gauges along Florida's west coast. The observed storm surge at the gauges was compared with the storm surge computed by the SLOSH model. Time series of the SLOSH model winds were compared with time series based on the analyzed wind field at each tide gauge, because in most cases there were no wind observations available at these gauges. The comparisons of the analyzed and modeled winds and the observed and modeled storm surge show that the SLOSH model reasonably repres...

For Williamstown tide gauge, at the northern‐most point of Port Phillip Bay (PPB), Melbourne, Victoria, tide registers from 1872 to 1966 and marigrams from 1950 to 1966, were digitized to extend sea‐level records back almost 100 years. Despite some vertical datum issues in the early part of the record, the data set is suitable for extreme sea‐level trend analysis after removal of the annual mean sea level. The newly digitized data was combined with the digital record to produce a combined record from 1872 to 2020. Analysis of this record revealed known problems of siltation of the tide gauge stilling well and associated reduction in tidal range at times during 1880–1895 and 1910–1940. A positive trend in tidal amplitude of 0.41 ± 0.01 mm yr−1 was found over 1966–2020, likely due to reduced hydraulic friction at the narrow entrance to PPB. Extreme sea‐level trends were examined over 1872–2020 for storm tides (the combination of storm surge and tide) after removal of the annual mean, and residuals after subtraction of the predicted tides. A non‐stationary Gumbel distribution with a time‐varying location parameter revealed statistically significant declining trends in the residuals of −0.73 ± 0.02 mm yr−1, consistent with the observed poleward movement of storm surge‐producing mid‐latitude weather systems. For storm tides a smaller declining trend of −0.40 ± 0.01 mm yr−1 was found. These trends are approximately an order of magnitude smaller than the current positive rates of mean sea level rise, meaning that storm tide hazard will continue to increase in the future. This information is relevant for future adaptation planning.

Prediction and reanalysis of storm surge rely on wind and pressure fields from either parametric tropical cyclone wind models or numerical weather model reanalysis, and both are subject to large errors during landfall. This study assesses two sets of wind/pressure fields for Hurricane Florence that made landfall along the Carolinas in September 2018 and appraises the impacts of differential structural errors in the two suites of modeled wind fields on the predictive accuracy of storm surge driven thereby. The first set was produced using Holland 2010 (H10), and the second set is the Hurricane Weather Research and Forecasting (HWRF) reanalysis created by the NWS National Centers for Environmental Prediction (NCEP). Each is validated using a large surface data set collected at public and commercial platforms and then is used as input forcing to a 2-D coastal hydrodynamic model (Delft3D Flexible Mesh) to produce storm surge along the Carolina coasts and major sounds. Major findings include the following. First, wind fields from HWRF are overall more accurate than those based on H10 for the periphery of the storm, though they exhibit limitations in resolving high wind speeds near the center. Second, applying H10 to the best track data for Florence yields an erroneously spike in wind speed on September 15th when the storm reduced to a tropical depression. Third, HWRF wind fields exhibit a progressively negative bias after landfall, likely due to deficiencies of the model in representing boundary layer processes, and to the lack of assimilation of surface product after landfall for compensating for these deficiencies. Fourth, using HWRF reanalysis as the forcings to Delft3D yields more accurate peak surges simulations, though there is severe underestimation of surge along the shoreline close to the track center. The peak surge simulations by Delft3D are biased low when driven by H10, even though over several locations the H10 model clearly overpredicts surface wind speeds. This contrast highlights the importance of resolving wind fields further away from the center in order to accurately reproduce storm surge and associated coastal flooding.

This study integrated coastal watershed models and combined them with a risk assessment method to develop a methodology to investigate the impact resulting from coastal disasters under climate change. The mid-western coast of Taiwan suffering from land subsidence was selected as the demonstrative area for the vulnerability analysis based on the prediction of sea level rise (SLR), wave run-up, overtopping, and coastal flooding under the scenarios of the years from 2020 to 2039. Databases from tidal gauges and satellite images were used to analyze SLR using Ensemble Empirical Mode Decomposition (EEMD). Extreme wave condition and storm surge were estimated by numerical simulation using the Wind Wave Model (WWM) and the Princeton Ocean Model (POM). Coastal inundation was then simulated via the WASH123D watershed model. The risk map of study areas based on the analyses of vulnerability and disaster were established using the Analytic Hierarchy Process (AHP) technique. Predictions of sea level rise, the maximum wave condition, and storm surge under the scenarios of 2020 to 2039 are presented. The results indicate that the sea level at the mid-western coast of Taiwan will rise by an average of 5.8 cm, equivalent to a rising velocity of 2.8 mm/year. The analysis indicates that the Wuqi, Lukang, Mailiao, and Taixi townships are susceptive, low resistant and low resilient and reach the high-risk level. This assessment provides important information for creating an adaption policy for the mid-western coast of Taiwan.

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.