Historical Storm Research Articles

Insurance loss model vs. meteorological loss index – how comparable are their loss estimates for European windstorms?

Abstract. Windstorms affecting Europe are among the natural hazards with the largest socio-economic impacts. Therefore, many sectors like society, the economy, or the insurance industry are highly interested in reliable information on associated impacts and losses. In this study, we compare – for the first time – estimated windstorm losses using a simplified meteorological loss index (LI) with losses obtained from a complex insurance loss (catastrophe) model, namely the European Windstorm Model of Aon Impact Forecasting. To test the sensitivity of LI to different meteorological input data, we furthermore contrast LI based on the reanalysis dataset ERA5 and its predecessor ERA-Interim. We focus on similarities and differences between the datasets in terms of loss values and storm rank for specific historical storm events in the common reanalysis period across 11 European countries. Our results reveal higher LI values for ERA5 than for ERA-Interim for all of Europe (by roughly a factor of 10), coming mostly from the higher spatial resolution in ERA5. The storm ranking is comparable for western and central European countries for both reanalyses, confirmed by high correlation values between 0.6 and 0.89. Compared to the Aon Impact Forecasting model, LI ERA5 shows comparable storm ranks, with correlation values ranging between 0.45 and 0.8. In terms of normalized loss, LI exhibits overall lower values and smaller regional differences. Compared to the market perspective represented by the insurance loss model, LI seems to have particular difficulty in distinguishing between high-impact events at the tail of the wind gust distribution and moderate-impact events. Thus, the loss distribution in LI is likely not steep enough, and the tail is probably underestimated. Nevertheless, it is an effective index that is suitable for estimating the impacts of storm events and ranking storm events, precisely because of its simplicity.

Mortality caused by tropical cyclones in the UnitedStates.

Natural disasters trigger complex chains of events within human societies1. Immediate deaths and damage are directly observed afteradisaster and are widely studied, but delayed downstream outcomes, indirectly caused by the disaster, are difficult to trace back to the initial event1,2. Tropical cyclones (TCs)-that is, hurricanes and tropical storms-are widespread globally and have lasting economic impacts3-5, but their full health impact remains unknown. Here we conduct a large-scale evaluation of long-term effects of TCs on human mortality in the contiguous UnitedStates (CONUS) for all TCs between 1930and2015. We observe a robust increase in excess mortality that persists for 15 years after each geophysical event. We estimate that the average TC generates 7,000-11,000 excess deaths, exceeding the average of 24 immediate deaths reported in government statistics6,7. Tracking the effects of 501 historical storms, we compute that the TC climate of CONUS imposes an undocumented mortality burden that explains a substantial fraction of the higher mortality rates along the Atlantic coast and is equal to roughly 3.2-5.1% of all deaths. These findings suggest that the TC climate, previously thought to be unimportant for broader public health outcomes, is a meaningful underlying driver for the distribution of mortality risk in CONUS, especially among infants (less than 1 yearof age), people 1-44 yearsof age, and the Black population. Understanding why TCs induce this excess mortality is likely to yield substantial health benefits.

Predicting storm surge extremes on the Southeast Brazilian Coast: Long-term projections with neural networks

Extreme sea level events, resulting from the confluence of tides and storm surges, pose a significant threat to coastal populations and economies. The escalating risks associated with these events are exacerbated by climate change, manifesting in heightened storm intensity, increased frequency, and rising sea levels. Precise estimation of the probability of extreme storm surges is crucial for effective coastal management and adaptation. However, utilizing historical storm data is challenging due to data scarcity and the imperative to consider potential non-stationarity induced by climate change in predicting such events. This study addresses these challenges by introducing two neural network-based machine learning systems: a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM). Leveraging local and remote atmospheric and oceanic conditions, these systems project storm surges until 2060, incorporating climate projections. Trained and evaluated using sea level data from Imbetiba Port in Macaé, Rio de Janeiro, Brazil, the models utilize dynamic regionalization data from the RegCM4 and WW3 models, forced by HadGEM2-ES and MPI climate models. Both neural network models exhibited similar performance patterns, demonstrating high agreement in predicting storm surge heights with a 100-year return value, based on Imbetiba Port data. Projections utilized peaks-over-threshold (POT) methods, and extremes were calculated using a generalized Pareto distribution (GPD). Long-term projections indicated a 28 % increase (MLP ANN) and a substantial 70 % increase (LSTM RNN) in estimating extreme values, surpassing the observed storm surge of 0.67 m. Projected mean values were 0.86 m for the MLP network and 1.15 m for the LSTM network, providing valuable insights into the potential amplification of extreme sea level events in the studied region.

Lake sediment record of eolian activity on the eastern Tibetan Plateau since 15 cal ka BP

Atmospheric dust has important influences on atmospheric circulation, global biogeochemical cycles, and hydrological processes. However, understanding the history of dust storms on the Tibetan Plateau (TP) remains challenging due to the lack of suitable geological archives. Lakes in dust-influenced regions act as dust repositories, offering the opportunity to trace the history of dust emissions and eolian activity. Here we present a synthesis of eolian activity on the eastern TP covering the past 15,000 years. It is based on records of grain size and n-alkanes from a sediment core from Gahai lake, which we combined with published pollen and other records from the same core, to reconstruct variations in surface runoff and eolian activity in this region. Our results indicate a correlation between vegetation conditions and eolian activity during different periods. Increased eolian activity occurred during the transition from the last deglaciation to the early Holocene, due to suboptimal vegetation conditions. Between 7.5 and 3.5 cal ka BP (ka), higher moisture levels resulted in the dominance of arboreal vegetation, which suppressed eolian activity. However, after 3.5 ka a sustained intensification of eolian activity occurred in the Gahai area, which was linked to decreasing vegetation cover, reduced regional humidity, and growing human impacts, especially in the eastern plateau, in southern Gansu. In recent decades, human interventions have suppressed eolian activity. Additionally, a ∼ 1435-year cyclicity in our record, and other regional records, suggests a link between increased eolian activity on the eastern TP and ice-rafting events in the North Atlantic. Generally, Holocene eolian dynamics were primarily influenced by the regional vegetation and climatic conditions which were controlled by the atmospheric circulation. However, in the late Holocene, climatic shifts and human influences had a synergistic effect which intensified the eolian activity, highlighting the important role of humans on recent dust dynamics in this region.

Superiority of artificial neural networks over conventional hydrological models in simulating urban catchment runoff

ABSTRACT The synergistic impacts of climate change and urbanisation have amplified the recurrence and austerity of intense rainfall events, exacerbating persistent flooding risk in urban environments. The intricate topography and inherent non-linearity of urban hydrological processes limit the predictive accuracy of conventional models, leading to significant discrepancies in flow estimation. Recent advancements in artificial neural network (ANNs) have demonstrated remarkable progress in mitigating most limitations, specifically in simulating complex, non-linear relationships, without an intricate comprehension of the underlying physical processes. This paper proposes a deep learning ANN-based flow estimation model for enhanced precision simulation of streamflow in urban catchments, with the research's distinctive contribution involving rigorous comparative evaluation of the developed model against the established Australian hydrological model, RORB. Gardiners Creek catchment, an urban catchment situated in East Melbourne was designated as the study area, with the model being calibrated upon historical storm incidences. The findings reveal that the ANN model substantially outperforms RORB, as evidenced by superior correlation, prediction efficiency, and lower generalisation error. This underscores the ANN's adeptness in accurately replicating non-linear-catchment responses to storm events, marking a substantial advancement over conventional modelling practices and indicating its transformative potential for enhancing flood prediction precision and revolutionising current estimation practices.

A Physically Constrained Model-Based Moisture Amplification Approach for Probable Maximum Precipitation (PMP) Estimation

Abstract The flood that would result from the greatest depth of precipitation “meteorologically possible” or probable maximum precipitation (PMP) is used in the design of dam spillways and other high-risk structures. Historically, PMP has been estimated by scaling precipitation totals obtained from severe historical storms, assuming more moisture could have been available. Over the last decade, numerical weather prediction models have been used to instead predict precipitation resulting from the addition of moisture in the simulations [called relative humidity maximization (RHM)]. Despite the major improvement they represent, two important barriers limit the applicability of model-based methods: first, the existence of different moisture amplification approaches that produce different estimates, and second, the need for a regional implementation of those techniques that were developed for individual basins. Taking Oregon’s mountainous coastal watersheds affected by atmospheric river storms as a case study, we develop a moisture amplification approach, which we call relative humidity perturbation (RHP) ratio that is physically constrained by historical maximum moisture. We find that both the magnitude and location of moisture increase matter and that RHP ratio produces lower amplified precipitation totals but storms that are more consistent with observed events than other methods such as RHM. We additionally find that it is possible to position a storm near-optimally over several basins in a homogeneous area, enabling the production of regional PMP estimates. The understanding we develop of the control moisture exerts on PMP-magnitude precipitation totals allows us to develop a more physically based methodology for the development of reliable storm amplification guidance.

Evaluations of storm tide hazard along the coast of China using synthetic dynamic tropical cyclone events

Concurring with high astronomical tides, storm surges have caused devastating damage in low-lying areas along the Chinese coastal regions. However, accurately calculating tropical cyclone (TC) storm tide hazards, especially those with long return periods, has proven challenging due to limited temporal and spatial information on TCs. In this study, we adopt the Synthetic Dynamic TC Method (SDTM), which enables a more robust estimation of storm tide hazards by generating a large number of synthetic TCs based on historical best track data and ocean-atmosphere environmental data. Within the SDTM framework, synthetic TCs corresponding to 10,000 years are validated using several statistical metrics, and the associated storm tides are simulated. For comparison, we employ the Historical Storm Method (HSM) to simulate storm tides for historical TCs from 1950 to 2019. Storm tide hazard curves are calculated and compared using these two methods. Our results demonstrate that the SDTM can robustly estimate storm tide hazards for both short and long return periods, whereas the HSM performs well for short return periods but struggles to reliably assess storm tide hazards for long return periods. Notably, within the SDTM, storm tide height exhibits nonlinear growth with increasing return periods in the Gumbel plot, a phenomenon not observed in the HSM due to the limited time span of TC records. With sufficient TC data, the spatial storm tide hazard maps obtained from the SDTM can serve as a robust foundation for developing disaster prevention and mitigation policies.

Integrating historical storm surge events into flood risk security in the Copenhagen region

Rapid urbanisation along the coasts of the world in recent decades has increased their vulnerability to storm surges, especially in response to mean sea level rise. The unique geographical and social conditions of Copenhagen, a major European coastal city, have prompted urban expansion along Køge Bay to the south of the city. However, this new urbanisation area is confronted with the common obstacle of developing a coastal defence strategy, i.e., the lack of long-term observational data required to determine a reliable storm surge protection level. This study aims to address this issue by developing a framework that integrates historical records of extreme storm surge events into coastal defence strategies, using Copenhagen as a case study. We propose a four-step work framework, including (1) Data collection and analysis: We collected and analysed data from neighbouring cities and used modelling and reanalysis data sets. By combining these sources, we aim to reconstruct historical time series for the study site dating back to 1836. This extended information set enhances our understanding of past storm surge events. (2) Statistical modelling and forecasting: Using Bayesian statistical methods, we fitted the historical storm surge data to appropriate probability distributions. This enabled us to generate probabilistic forecasts of storm surge magnitudes, providing insight into the likelihood of future events and their potential impacts on the coastal area. (3) Sensitivity analyses: We performed sensitivity experiments using Markov chain Monte Carlo (MCMC) methods to identify the most influential parameters, such as thresholds, that affect storm surge levels. This analysis improved our understanding of the key drivers of storm surge events and their uncertainties, further informing coastal defence strategies. (4) Expert judgement and risk management: Expert judgements are implemented to establish the necessary security level to manage flood risks in the city. This helps to ensure that high-impact, low-probability events are adequately considered in risk management efforts. Following this framework, we can develop a comprehensive understanding of storm surge risks in the urbanised region south of Copenhagen and use historical data to inform coastal defence strategies. This study emphasises the importance of incorporating long-term observational data and expert insights to improve the resilience of coastal cities facing the challenges of urbanisation and climate change.

Tropical cyclone activity over the past 1200 years at the Pelican Cays, Belize

Tropical cyclone (TC) models indicate that continued planet warming will likely increase the global proportion of powerful TCs (specifically Categories 4 and 5 hurricanes), increasingly jeopardizing low-lying coastal communities and resources such as the Pelican Cays, Belize. The combination of increased coastal development and continued relative sea-level rise puts these communities at even higher risk of damage from TCs. The short TC observational record for the western Caribbean hampers the extensive study of TC activity on centennial timescales, which hinders our ability to fully understand past TC climatology and improve the accuracy of TC models. To better assess TC risk, paleotempestological studies are necessary to put future scenarios in perspective. Here, we present a high-resolution reconstruction of coarser-grained sediment deposits associated with TC (predominately ≥ Category 2 hurricanes) passages over the past 1200 years from Elbow and Lagoon Cays, two coral reef-bounded lagoons at the northern and southern end of the Pelican Cays; the most southern Belizean paleotempestological site to date. Coincident timing of historic storms with statistically significant coarser-grained deposits within cay lagoon sediment cores allows us to determine which historic TCs likely generated event layers (tempestites) archived in the sediment record. Our compilation frequency analysis indicates one active interval (above-normal TC activity) from 1740-1950 CE and one quiet interval (below-normal TC activity) from 850-1018 CE. The active and quiet intervals in the Pelican Cays composite record are anticorrelated with those from nearby and re-analyzed TC records to the north, including the Great Blue Hole (∼100 km north) and the Northeast Yucatan (∼380 km northwest). This site-specific anticorrelation in TC activity along the western Caribbean indicates that we cannot rely on any one single TC record to represent regional TC activity. However, we cannot discount that these anticorrelated periods between the western Caribbean sites are due to randomness. To confirm that the anticorrelation in TC activity among sites from the western Caribbean is indeed a function of climate change and not randomness, an integration of more records and TC model simulations over the past millennium is necessary to assess the significance of centennial-scale variability in TC activity recorded in reconstructions from the western Caribbean.

Observation and modelling of infragravity waves at a large meso-tidal inlet and lagoon

The role of infragravity waves (IG waves) in beach and dune erosion or in flood hazard has been extensively studied on open beaches. In contrast, the detailed characterization of IG waves and their contribution to the Total Water Level (TWL) along the shore of inlets received little attention so far. In such environment, there is a real lack of in situ observations of waves and hydrodynamics conditions at appropriate spatial and temporal coverage to study the role of infragravity (IG) waves (long waves of frequency typically ranging between 0.004 Hz and 0.04–0.05 Hz) on coastal hazards. This contribution is based on field observations collected at the Arcachon Lagoon, a shallow semi-enclosed lagoon connected to the ocean by a large tidal inlet, located in southwest France. Analyses combine observations made at several locations during storm events within the inlet and the lagoon with numerical simulation with the XBeach surfbeat model to explore the spatial variability of IG waves and simulate observed, historical, and idealized storm conditions. The results show that IG waves are substantial during typical winter storms at the inlet and range from Hm0 = 0.8 to over 1 m across the ebb delta and about 0.4–0.6 m in the inner part of the inlet. At the lagoon entrance, IG waves remain substantial (about 0.1–0.2 m) and decrease to a few centimeters at the lagoon shore. The spatial variability and magnitude of IG waves along the inlet coast, simulated for the historical storms, are quite comparable to those observed during classical winter, and do not increase linearly with offshore wave energy. However, both observations and simulations reveal local amplifications of IG waves in the inner part of the inlet, especially along the sheltered coast were IG waves dominate the variance of free surface elevation, reaching about 0.6–0.7 m during common storms and more than 1 m for an extreme storm scenario. A numerical experiment indicates that IG wave reflection from one coast to the other contributes up to 35–40% of the measured IG wave height at a hot spot located along the sheltered coast. Finally, the contribution of IG waves to TWL at the shore on both sides of the inlet has been estimated to be about 0.4–0.6 m for a common storm and 0.6–0.9 m for an extreme scenario, locally peaking at 0.74 and 1.1 m respectively and overpassing the contribution of wave-induced setup. This work provides new insights into the contribution of IG waves to TWL and its implications for overtopping flooding hazard and overwash processes at large inlets, highlighting the need to consider IG waves in Early Warning Systems or hazard mapping for flood prevention plans in these environments.

Comparative analysis of the variability and impacts of tropical cyclones in flood-prone areas of Zimbabwe

Tropical cyclones (TCs) are extreme meteorological events that cause significant deaths, infrastructure damage, and financial losses around the world. In recent years, the Eastern Highlands of Zimbabwe's have become increasingly vulnerable to TCs caused by Indian Ocean tropical cyclones making landfall more frequently. There is still a limited understanding of the phenomenon and the quantification of its impacts. The aim of this research is to conduct a comparative analysis of the variability in the severity of tropical cyclones by analysing historical storm tracks and mapping the environmental impacts in Zimbabwe's Chimanimani and Chipinge districts. Results indicate that, between 1945 and 2022, the Eastern Highlands of Zimbabwe experienced 5 of the total 865 cyclones in the Southwest Indian Ocean. The maximum sustained winds from the Cyclone Idai in the Eastern Highlands were recorded as 195 km/h. Some of the remote sensing-based indices used to extract spatial information about the condition of vegetation, wetlands, built-up area, and bar land during pre and post cyclonic events included the Normalized Difference Vegetation Index (NDVI) and Modified Normalized Difference Water Index (MNDWI). Analysis of NDVI in the Eastern Highlands revealed that there was a significant decrease in vegetated area because of the cyclone impact, with a decrease of 2.1% and 16.68% for cyclone Japhet and Idai respectively. The MNDWI shows a 10.74% increase in water content after cyclone Eline. Field validation in 2019 confirms the research findings. An Operations Dashboard Disaster Management System was developed in order to disseminate information to the affected stakeholders about the potential risk that the face due to the occurrence of the natural phenomena.

The preservation of storm events in the geologic record of New Jersey, USA

ABSTRACTGeologic reconstructions of overwash events can extend storm records beyond the brief instrumental record. However, the return periods of storms calculated from geologic records alone may underestimate the frequency of events given the preservation bias of geologic records. Here, we compare a geologic reconstruction of storm activity from a salt marsh in New Jersey to two neighboring instrumental records at the Sandy Hook and Battery tide gauges. Eight overwash deposits were identified within the marsh's stratigraphy by their fan‐shaped morphology and coarser mean grain size (3.6 ± 0.7 φ) compared to autochthonous sediments they were embedded in (5.6 ± 0.8 φ). We used an age–depth model based on modern chronohorizons and three radiocarbon dates to provide age constraints for the overwash deposits. Seven of the overwash deposits were attributed to historical storms, including the youngest overwash deposit from Hurricane Sandy in 2012. The four youngest overwash deposits overlap with instrumental records. In contrast, the Sandy Hook and Battery tide gauges recorded eight and 11 extreme water levels above the 10% annual expected probability (AEP) of exceedance level, respectively, between 1932/1920 and the present. The geologic record in northern New Jersey, therefore, has a 36–50% preservation potential of capturing extreme water levels above the 10% AEP level.

A dataset of storm surge reconstructions in the Western North Pacific using CNN

The relatively short duration of available tide gauge records poses challenges for conducting comprehensive statistical analyses of storm surges in the Western North Pacific. To address this issue, we employ a convolutional neural network model to reconstruct the maximum daily storm surge at 160 tide gauges from 1900 to 2010 in the Western North Pacific. The reconstructed dataset serves multiple purposes. Firstly, it facilitates the identification of regions where notable changes in the storm surges have occurred in the past. Additionally, the dataset enables long-term analyses of the storm surge climate, offering insights into historical patterns and variations. Furthermore, it provides a solid foundation for conducting robust extreme value analyses. To ensure accessibility, the data are publicly available through a repository, allowing for easy access and utilization by the broader scientific community and the general public. Overall, our research contributes to the field of oceanography by providing a dataset that aids in understanding the historical storm surge dynamics in the Western North Pacific region.

Tropical or extratropical cyclones: what drives the compound flood hazard, impact, and risk for the United States Southeast Atlantic coast?

Subtropical coastlines are impacted by both tropical and extratropical cyclones. While both may lead to substantial damage to coastal communities, it is difficult to determine the contribution of tropical cyclones to coastal flooding relative to that of extratropical cyclones. We conduct a large-scale flood hazard and impact assessment across the subtropical Southeast Atlantic Coast of the United States, from Virginia to Florida, including different flood hazards. The physics-based hydrodynamic modeling skillfully reproduces coastal water levels based on a comprehensive validation of tides, almost two hundred historical storms, and an in-depth hindcast of Hurricane Florence. We show that yearly flood impacts are two times as likely to be driven by extratropical than tropical cyclones. On the other hand, tropical cyclones are 30 times more likely to affect people during rarer 100-year events than extratropical cyclones and contribute to more than half of the regional flood risk. With increasing sea levels, more areas will be flooded, regardless of whether flooding is driven by tropical or extratropical cyclones. Most of the absolute flood risk is contained in the greater Miami metropolitan area. However, several less populous counties have the highest relative risks. The results of this study provide critical information for understanding the source and frequency of compound flooding across the Southeast Atlantic Coast of the United States.

Use of Artificial Intelligence Modelling for the Dynamic Simulation of Urban Catchment Runoff

The complex topography and inherent nonlinearity affiliated with influential hydrological processes of urban catchments, coupled with limited availability of measured data, limits the prediction accuracy of conventional models. Artificial Neural Network models (ANNs) have displayed commendable progress in recognising and simulating highly complex, non-linear associations allied with input-output variables, with limited comprehension of the underlying physical processes. Therefore, this paper investigates the effectiveness and accuracy of ANN models, in estimating the urban catchment runoff, employing minimal and commonly available hydrological data variables – rainfall and upstream catchment flow data, employing two powerful supervised-learning-algorithms, Bayesian-Regularization (BR) and Levenberg-Marquardt (LM). Gardiners Creek catchment, encompassed in Melbourne, Australia, with more than thirty years of quality-checked rainfall and streamflow data was chosen as the study location. Two significant storm events that transpired within the last fifteen years - the 4th of February 2011 and the 6th of November 2018, were nominated for calibration and validation of the ANN model. The study results advocate that the use of the LM-ANN model stipulates accurate estimates of the historical storm events, with a stronger correlation and lower generalisation error, in contrast to the BR-ANN model, while the integration of upstream catchment flow alongside rainfall, vindicate for their collective impact upon the dynamics of the flow being spawned at the downstream catchment locations, significantly enhancing the model performance and providing a more cost-effective and near-realistic modelling approach that can be considered for application in studies of urban catchment responses, with limited data availability.

High-resolution, open-source modeling of inland flooding impacts on the North Carolina bulk electric power grid

Although damages to local distribution systems from wind and fallen trees are typically responsible for the largest fraction of electricity outages during hurricanes, outages caused by flooding of electrical substations pose a unique risk. Electrical substations are a key component of electric power systems, and in some areas, the loss of a single substation can cause widespread power outages. Before repairing damaged substations, utilities must first allow floodwaters to recede, potentially leaving some customers without power for weeks following storms. As economic losses from flooding continue to increase in the U.S., there has been increasing attention paid to the potential impacts of flooding on power systems. Yet, this attention has mostly been limited to geospatial risk assessments that identify what assets are in the path of flooding. Here, we present the first major attempt to understand how flooding from hurricanes and other extreme precipitation events affects the dynamic behavior of power networks, including losses of demand and generation, and altered power flows through transmission lines. We use North Carolina, hit by major hurricanes in three of the past seven years, as a test case. Using open-source data of grid infrastructure, we develop a high-resolution direct current optimal power flow model that simulates electricity production and generators and power flows through a network consisting of 662 nodes and 790 lines. We then simulate grid operations during the historical (2018) storm Hurricane Florence. Time series of flooding depth at a discrete set of ‘high water’ mark points from the storm are used to spatially interpolate flooding depth across the footprint area of the storms on an hourly basis. Outages of substations and solar farms due to flooding are translated to location-specific losses of demand and solar power production throughout the network. We perform sensitivity analysis to explore grid impacts as a function of the height of sensitive equipment at substations. Results shed light on the potential for localized impacts from flooding to have wider impacts throughout the grid (including in areas not affected by flooding), with performance tracked in terms of transmission line flows/congestion, generation outputs, and customer outages.

Quantitative assessment of building risks and loss ratios caused by storm surge disasters: A case study of Xiamen, China

China is severely affected by storm surge disasters, which result in substantial economic losses and casualties in coastal regions. Assessing the risk of storm surge disasters can provide valuable insights into the expected losses and severity of future impacts, offering critical foresight for disaster prevention and mitigation strategies. This study assesses the quantitative risk of storm surge disasters, focusing on coastal buildings since they are particularly susceptible to storm surges and frequently bear the brunt such disasters. Xiamen city, China, was used as case study. A high-precision numerical model, using Finite Volume Community Ocean Model (FVCOM), was developed to simulate inundation during storm surges. By referencing historical storm surge records, we defined key parameters for probable maximum typhoon-induced storm surge (PMTSS) in Xiamen. These parameters were used to calculate the corresponding inundation range and water depth distribution within the region. Subsequently, the results were integrated with vulnerability curves that represent the susceptibility of buildings in Xiamen to storm surge-induced damage, enabling the quantitative risk assessment for associated loss risks. The study findings offer valuable guidance for urban planning and functional layout design in coastal areas. Furthermore, the findings contribute to understanding storm surge disaster risks and facilitating informed decision-making processes, ultimately enhancing disaster preparedness and resilience in vulnerable coastal regions.

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.

Adaptive multi-fidelity Monte Carlo for real-time probabilistic storm surge predictions

Real-time, probabilistic predictions of the expected storm surge represent an important information source for guiding emergency response decisions during landfalling tropical storms/cyclones. The uncertainty quantification in these predictions is accomplished as follows: an ensemble of sample storm scenarios is generated based on the nominal storm advisory and a probabilistic description of the corresponding forecast errors; subsequently high-fidelity numerical simulations are performed to predict the surge for each of these scenarios; the simulation results are finally leveraged to estimate the statistics of interest. This process is repeated whenever a new storm advisory is issued. The need to establish predictions in real-time, to support emergency planning decisions, creates incentives for high computational efficiency for this probabilistic estimation. This paper investigates an adaptive Multi-Fidelity Monte Carlo (MFMC) framework for achieving such an objective. As a lower-fidelity model within the MFMC setup, a surrogate model is adaptively developed based on high-fidelity numerical simulations from the current or past storm advisories, achieving information sharing across them. MFMC leverages the correlation between the high- and low-fidelity models to establish unbiased predictions with high statistical accuracy, significantly improving abilities to provide timely and informative estimates. To facilitate the development of the low-fidelity model using only a small number of high-fidelity numerical simulations, a combination of physics-based and data-driven dimensionality reduction techniques are introduced for the input and the output, respectively, of the surrogate model. To accommodate the use of high-fidelity simulations from the current advisory in the surrogate model calibration, the MFMC implementation is established using leave-one-out surrogate model predictions. Finally, the challenge of establishing MFMC predictions for a large number of quantities of interest (QoIs), corresponding to the surge at different geographic locations, is discussed. These QoIs may advocate conflicting decisions for the optimal MFMC implementation, and an efficient search for a compromise solution is introduced. The efficiency of the proposed MFMC framework is showcased by considering its application to different historical storms.

Impact of tidal dynamics and typhoon-induced inundation on saltwater intrusion in coastal farms

The increase in storm surge events caused by climate change exacerbates adverse effects on seawater inundation in coastal areas. An accurate description of the water level curve is crucial for understanding the process of saltwater intrusion (SWI) resulting from storm surge. Most studies involving empirical surges as inputs to groundwater models, often simplify spatial and temporal seawater inundation processes, which may increase the uncertainty in vertical seawater intrusion. To address this gap, we employed a comprehensive modeling approach using storm surge model ADCIRC and numerical simulator HydroGeoSphere to reveal SWI dynamics during a historical storm surge event in a coastal farm, considering varying tidal-surge phases and typhoon intensities. Our findings indicate pronounced SWI variations even with consistently highest water level during a storm surge, contingent on prior tidal processes. The timing of typhoon landfall on an hourly scale yielded diverse water level curves, altering the function of SWI. Intriguingly, SWI exacerbates following a high tide with 31.2 % average salinity higher, highlighting the profound modulation effect of tidal levels on SWI. Local topography significantly influenced SWI dynamics. Ponds, for instance, retained elevated salinity levels for over 15 h, indicating a more prolonged exposure to salinity than roads. These findings underscore the importance of considering both tidal influences and topographical factors in understanding and mitigating SWI in coastal agricultural management.