Surge Levels Research Articles

Brief communication: From modelling to reality – flood modelling gaps highlighted by a recent severe storm surge event along the German Baltic Sea coast

Abstract. In October 2023, the Baltic Sea coasts of Germany and Denmark experienced a severe storm surge, predominantly impacting the German state of Schleswig-Holstein and parts of southern Denmark. The surge led to extensive flooding in cities like Flensburg and Schleswig, causing the breaching of at least six (regional) dikes and causing over EUR 200 million in damages in Schleswig-Holstein. By chance, the peak water levels of this storm surge aligned well with those of recent hydrodynamic flood modelling studies of the region. This rare coincidence offers crucial insights for our understanding of flooding impacts, flood management, and modelling. By comparing those studies to the real-world example using extensive media reports, we aim to extract key insights and identify gaps to be tackled in order to improve flood risk modelling in the Baltic Sea region and beyond.

Nonlinear Tide‐River‐Surge Interactions and Their Impacts on Compound Flooding During Typhoon Hato in the Pearl River Delta

AbstractIn coastal areas, multiple hydrodynamic processes co‐occur, including the astronomical tide, river discharge, and storm surge. Their propagation and interaction within coastal tidal river result in strong nonlinear behavior in inland areas. This study employed a hydrodynamic model and continuous wavelet transform analysis to investigate the complex tide‐river‐surge interactions and their impacts on compound flooding in the West River of the Pearl River Delta during an extreme typhoon event. Validated model outputs provided insights into the spatial and temporal variations of river discharge, water levels, and currents. Wavelet analysis revealed river discharge modulates tidal properties, causing nonstationary tides and asymmetries, with high flows suppressing tidal ranges but facilitating energy transfers to overtides. During Typhoon Hato, storm surges dominated high‐frequency water level fluctuations that rapidly propagated upstream. Crucially, strong high‐frequency tide‐river‐surge coupling induced significant water level amplifications, with interaction intensities increasing landward. In upstream areas where riverine and coastal drivers converged, flood risks exceeded typical estimates due to this vigorous multi‐driver compounding. Findings highlighted how existing flood mitigation approaches over simplistically superposing individual sources may severely underestimate flood levels by neglecting such nonlinear interactions. A comprehensive accounting of the cumulative, multiplicative effects of tides, discharge, storm surge, and sea level rise is imperative. This quantitative unraveling of key physical drivers offers transferable insights applicable to compound flood risk evaluations and policy guidance for enhancing resilience in other estuary and delta regions. Future work should focus on holistically modeling multivariate extremes and their interactions.

Spatially seamless and temporally continuous assessment on compound flood risk in Hong Kong

Compound flooding results from the simultaneous occurrence of extreme storm surges, sea level rise, and heavy rainfall. These events often lead to impacts significantly more severe than those caused by any individual flood-inducing factor alone. However, the limited and sparse data from tidal gauges hampers precise risk assessment at ungauged sites in coastal cities. Our study addresses this gap by integrating ensemble machine learning with Bayesian inference, offering a comprehensive spatial–temporal analysis of compound flood risk from 1979 to 2022 in Hong Kong. We developed an ensemble machine learning approach within the Bayesian hierarchical modeling framework to achieve spatial–temporal continuity in the estimation of extreme storm surges and mean sea level at sites without tidal gauge stations. Results show a significant yearly increase in maximum storm surge levels by 3 mm and a significant rise in mean sea level of 25 mm per decade in Hong Kong. Our analysis also indicates a significant increase in daily heavy rainfall intensity. Furthermore, in 14.54 % of cases, extreme storm surges coincided with heavy rainfall, while 13.69 % of heavy rainfall events occurred alongside extreme sea level conditions. The copula-based joint analysis reveals significant positive correlations among these extreme events. Our findings further reveal that the return level for a 100-year heavy rainfall event increases dramatically from 126.36 mm in the univariate case to 261.16 mm in the trivariate scenario, underlining the escalated risk associated with compound flooding. Similarly, for storm surge extremes, trivariate analysis reveals elevated risk during compound flood events, with the return level rising from 1.18 m (univariate scenario) to 1.40 m (trivariate scenario) for a 100-year return period. These spatial–temporal maps and comprehensive compound flood risk assessments offer crucial insights for addressing the multi-hazard flood risk in coastal urban areas.

Reproductive outcomes in fresh transfer cycles and antagonists with premature luteinizing and/or progesterone surge: a single center retrospective cohort study.

The optimal outcome of assisted reproductive technology is a successful live birth after fresh embryo transfer. However, the success pregnancy rate of fresh embryo transfer cycle in antagonist protocol is lower than that observed in other protocols. Despite the use of antagonists (GnRH-ant), the incidence of luteinizing hormone surge and elevated progesterone levels remain at approximately 5%-38%. Progesterone is widely recognized to exert adverse effects on fresh embryo transfer outcomes. This study aimed to investigate the impact of luteinizing hormone surge and progesterone levels on live birth rate following fresh embryo transfer and explore appropriate progesterone thresholds to enhance pregnancy outcomes. This retrospective cohort study included a total of 1,177 antagonist protocol cycles with fresh embryo transfer. The patients were divided into four groups based on the presence of premature LH surge and progesterone level on trigger day>1.5ng/ml. Then, the relationship between the variables and the pregnancy outcome was analyzed and compared in each group. The transient rise of luteinizing hormone did not impact pregnancy outcomes (P=0.345; P=0.3; P=0.787), in contrast to progesterone levels on the day of hCG administration (P=0.047*; P=0.015*; P=0.021*). In cases with luteinizing hormone surge, elevated progesterone levels were correlated with higher antral follicle count (AFC), and as progesterone levels increased, a greater quantity of oocytes and embryos were obtained. However, there was no statistically significant difference in pregnancy outcomes. In cases without luteinizing hormone surge, elevated progesterone levels led to significantly poorer pregnancy outcomes. Furthermore, the curve-fitting and threshold-effect analysis revealed a notable decline in live birth rates when progesterone exceeded or equaled 1.10ng/ml (OR, 0.25; 95% CI, 0.09-0.66; P = 0.005*). The GnRH-ant dosage addition should be carefully selected in flexible antagonist protocols. The presence of elevated progesterone levels may be associated with improved embryo quality when luteinizing hormone surge occurred. In the absence of a luteinizing hormone surge, progesterone levels showed a larger impact on the pregnancy outcome, and fresh embryo transfer should not be performed if the progesterone level on the day of hCG administration is higher than 1.10ng/ml.

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

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

Vulnerability of endemic insular mole skinks to sea‐level rise

AbstractAlthough coastal islands are home to many endemic species, they are also at risk of inundation from storm surge and sea level rise. Three subspecies of mole skink (Plestiodon egregius egregius, P. e. insularis, and the Egmont Key Mole Skink known from a single occurrence) occur on a small number of islands off the Gulf Coast of Florida, USA. We used the most recent sea level rise projections and the latest storm surge simulation data to predict impacts to habitat for insular mole skinks in Florida from 2030 to 2150. Our models predicted that in <100 years (by 2100; intermediate sea level rise scenario; ~1.08–1.15 m sea level rise), >78% of preferred habitat for the Florida Keys Mole Skink, >65% of preferred habitat for the Cedar Key Mole Skink, and >36% of preferred habitat for the Egmont Key Mole Skink will be inundated from sea level rise. Storm surge from tropical cyclones presents a more immediate risk to insular mole skink habitat: our models predicted that between 58% and 75% of Florida Keys Mole Skink habitat is at risk of being submerged under an average maximum of between 0.60 (SD = 0.86) and 0.98 (SD = 0.36) m of storm surge water for a category 1 storm, and the amount of habitat predicted to be impacted increases for higher intensity storms. Our models predicted similar trends for Cedar Key and Egmont Key Mole Skink habitat. Given current sea level rise projections, our models predicted that all three subspecies could be extinct by 2140 due to habitat inundation. There remains uncertainty about how species and ecosystems will respond to sea level rise, thus research to fill these gaps could help mitigate the effects of sea level rise in areas most vulnerable to the effects of climate change.

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.

A study of ten tide gauge records from the Clyde in Western Scotland

ABSTRACT Sea level information, and its understanding, are essential for research in oceanography and coastal engineering. This paper describes sea level variability in the Clyde using data from 10 tide gauges. High coherence of sea level variability is found between the Firth of Clyde and estuary of the River Clyde, and between the Firth and estuary and the upstream part of the river. Consequently, if gaps exist in any of the records, missing information can be inferred from others. This applies to timescales of typically hours and longer, appropriate for storm surge or mean sea level change studies. No evidence exists for ‘see-saw’ oscillations of the river on subtidal timescales, as reported for the River Thames, but higher-frequency variability (typical period 45 minutes) is found with equal, or opposite, phase at different river locations. Such a standing wave behaviour would be one of the few reported examples of ‘river seiches’. Data from several agencies are used: two gauges of the UK National Network of the Environment Agency, five from the Scottish Environment Protection Agency, and three from Peel Ports. Only the former tend to be used in academic studies, but the present paper has demonstrated how useful complementary sources can be.

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

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

Global application of a regional frequency analysis to extreme sea levels

Abstract. Coastal regions face increasing threats from rising sea levels and extreme weather events, highlighting the urgent need for accurate assessments of coastal flood risk. This study presents a novel approach to estimating global extreme sea level (ESL) exceedance probabilities using a regional frequency analysis (RFA) approach. The research combines observed and modelled hindcast data to produce a high-resolution (∼1 km) dataset of ESL exceedance probabilities, including wave setup, along the entire global coastline (excluding Antarctica). The methodology presented in this paper is an extension of the regional framework of Sweet et al. (2022), with innovations introduced to incorporate wave setup and apply the method globally. Water level records from tide gauges and a global reanalysis of tide and surge levels are integrated with a global ocean wave reanalysis. Subsequently, these data are regionalised, normalised, and aggregated and then fit with a generalised Pareto distribution. The regional distributions are downscaled to the local scale using the tidal range at every location along the global coastline obtained from a global tide model. The results show 8 cm of positive bias at the 1-in-10-year return level when compared to individual tide gauges. The RFA approach offers several advantages over traditional methods, particularly in regions with limited observational data. It overcomes the challenge of short and incomplete observational records by substituting long historical records with a collection of shorter but spatially distributed records. These spatially distributed data not only retain the volume of information but also address the issue of sparse tide gauge coverage in less populated areas and developing nations. The RFA process is illustrated using Cyclone Yasi (2011) as a case study, demonstrating how the approach can improve the characterisation of ESLs in regions prone to tropical cyclone activity. In conclusion, this study provides a valuable resource for quantifying the global coastal flood risk, offering an innovative global methodology that can contribute to preparing for – and mitigating against – coastal flooding.

Phytochemicals-based investigation of Rubia cordifolia pharmacological potential against letrozole-induced polycystic ovarian syndrome in female adult rats: In vitro, in vivo and mechanistic approach

Polycystic Ovarian Syndrome (PCOS) is a metabolic, reproductive, and endocrine disorder affecting women of fertile age. This study aimed to formulate a phytochemicals-based standardized aqueous ethanolic extract of Rubia cordifolia (SERC) to explore its pharmacological potential in PCOS-induced female rats and elucidate its mechanism. HPLC analysis revealed the presence of phytochemicals such as chlorogenic acid, p-coumaric acid, gallic acid, and kaempferol. Thirty female adult rats were divided into two groups for induction of PCOS (5 female rats in the normal control group + 25 female rats in the disease-induced group). PCOS was induced by administering letrozole (1 mg/kg p.o.) for 6 weeks. After PCOS induction, animals of the disease-induced group were divided into five groups: one group used as disease control (PCOS) group, one group on metformin (20 mg/kg), and three groups on SERC (200, 400, and 600 mg/kg). Histopathological analysis showed that PCOS induction reduced corpus luteum and developing follicles and increased cystic follicles. In comparison, SERC treatment improved ovulation with more primary and developing follicles. SERC reduced the serum insulin, LH surge, and testosterone levels while improving the FSH, estrogen, and progesterone serum levels. SERC significantly improved the oxidation status of the liver and normalized the lipid profile and liver function markers. In conclusion, SERC treated PCOS, and the suggested mechanism might be the restoration of aromatase activity and background inflammatory status improvement in ovaries.

Quantifying Compound and Nonlinear Effects of Hurricane‐Induced Flooding Using a Dynamically Coupled Hydrological‐Ocean Model

AbstractWe recently developed a dynamically coupled hydrological‐ocean modeling system that provides seamless coverage across the land‐ocean continuum during hurricane‐induced compound flooding. This study introduced a local inertial equation and a diagonal flow algorithm to the overland routing of the coupled system’s hydrology model (WRF‐Hydro). Using Hurricane Florence (2018) as a test case, the performance of the coupled model was significantly improved, evidenced by its enhanced capability of capturing backwater and increased water level simulation accuracy and stability. With four model experiments, we present a framework to detangle, define, and quantify compound and nonlinear effects. The results revealed that the flood peaks in the lower Cape Fear River Basin and the coastal waters were contributed by inland flooding and storm surge, respectively. These two processes had comparable contributions to the flooding in the Cape Fear River Estuary. The compound effect was identified when the flood levels resulting from the combination of land and ocean processes surpassed those caused by an individual process alone. The compound effect during Hurricane Florence exhibited limited impact on flood peaks, primarily due to the time lag between the peaks of the storm surge and the inland flooding. In the period between the two peaks, the compound effect was salient and significantly impacted the magnitude and variation of the flood level. The nonlinear effect, defined as the difference between the compound flood level and the superposition of storm surge and inland flooding water levels, reduced flood levels in the river channels while increasing flood levels on the floodplain.

Public preferences for coastal adaptation: Economic evidence from a discrete choice experiment for hard structures and nature-based solutions in Miami, Florida

Coastal communities are under pressure to adapt to the increasing impacts of climate change and environmental degradation. One strategy to achieve adaptation goals is to deploy hard structures and nature-based solutions that can help reduce damages from flooding, storm surge, and sea level rise in coastal areas. Understanding how local communities perceive and value these measures helps local authorities follow strategies that not only increase efficiency, but also garner public support. In this paper, we used Miami-Dade County as a case study to establish public preferences and trade-offs across hard structures and nature-based solutions and their physical characteristics to address coastal risk reduction: specifically, seawalls and mangroves. By applying a discrete choice experiment, we showed that respondents are generally in favor of deploying any coastal measures, with a higher willingness-to-pay for nature-based solutions over hard structures. We also found that the same respondents identify the length of protected coastline as a valuable feature of these coastal measures. Our findings inform ongoing plans and policies for coastal adaptation, particularly the deployment of nature-based solutions, and also illustrate the importance of including public input in the decision-making process of coastal management policy.

ESTROGEN LOWERS HEART RATE IN PRE-OVULATORY PHASE OF MENSTRUAL CYCLE IN WOMEN

Hormonal fluctuation in various phases of menstrual cycle may be an important regulatory factor of heart rate. Based on this proposition, an observational study was conducted in 81 women with normal reproductive health to find out the changes of heart rate in the pre-ovulatory (10-13th day of menstrual cycle) and post-ovulatory phases of menstrual cycle. Data were analyzed using Pearson correlation and paired t-test. The test for correlation coefficient using t-statistics showed that the changes of heart rate between pre-ovulatory and post-ovulatory phases was statistically significant (r= 0.79, R2=0.63 and p<0.01). On the other hand, the paired t-test (2 tails) revealed that the heart rate was significantly decreased (t= 3.86, df=80, Md=3.04 and p<0.01) in the pre-ovulatory phase when compared to the post-ovulatory phase. It is well documented that both estrogen and progesterone level are found to have moderate in luteal phase in relation to the pre-ovulatory phase during LH surge or peak estrogen level and it is approximately 10 times just 10-12 hours before ovulation. From the endocrinological view point, it can be concluded that the estrogen may have the heart rate lowering effects in pre-ovulatory phase in the study subjects.

A deep-learning model for rapid spatiotemporal prediction of coastal water levels

With the increasing impact of climate change and relative sea level rise, low-lying coastal communities face growing risks from recurrent nuisance flooding and storm tides. Thus, timely and reliable predictions of coastal water levels are critical to resilience in vulnerable coastal areas. Over the past decade, there has been increasing interest in utilizing machine learning (ML) based models for emulation and prediction of coastal water levels. However, flood advisory systems still rely on running computationally demanding hydrodynamic models. To alleviate the computational burden, these physics-based models are either run at small scales with high resolution or at large scales with low resolution. While ML-based models are very fast, they face challenges in terms of ensuring reliability and ability to capture any surge levels. In this paper, we develop a deep neural network for spatiotemporal prediction of water levels in coastal areas of the Chesapeake Bay in the U.S. Our model relies on data from numerical weather prediction models as the atmospheric input and astronomical tide levels, while its outputs are time series of predicted water levels at several tide gauge locations across the Chesapeake Bay. We utilized a CNN-LSTM setting as the architecture of the model. The CNN part extracts the features from a sequence of gridded wind fields and fuses its output to several independent LSTM units. The LSTM units concatenate the atmospheric features with respective astronomical tide levels and produce water level time series. The novel contribution of the present work is in spatiotemporality and in prioritization of the physical relationships in the model to maintain a high analogy to hydrodynamic modeling, either in the network architecture or in the selection of predictors and predictands. The results show that this setting yields a strong performance in predicting coastal water levels that cause flooding from minor to major levels. We also show that the model stands up successfully to the rigorous comparison with a high-fidelity ADCIRC model, yielding mean RMSE and correlation coefficient of 14.3 cm and 0.94, respectively, in two extreme cases, versus 12.30 cm and 0.96 for the ADCIRC model. The results highlight the practical feasibility of employing fast yet inexpensive data-driven models for resilient coastal management.

Improving our understanding of future tropical cyclone intensities in the Caribbean using a high-resolution regional climate model

The Caribbean region is prone to the strong winds and low air pressures of tropical cyclones and their corresponding storm surge that driving coastal flooding. To protect coastal communities from the impacts of tropical cyclones, it is important to understand how this impact of tropical cyclones might change towards the future. This study applies the storyline approach to show what tropical cyclones Maria (2017) and Dorian (2019) could look like in a 2 °C and 3.4 °C warmer future climate. These two possible future climates are simulated with a high-resolution regional climate model using the pseudo global warming approach. Using the climate response from these simulations we apply a Delta-quantile mapping technique to derive future changes in wind speed and mean sea level pressure. We apply this Delta technique to tropical cyclones Maria and Dorian’s observed wind and pressure fields to force a hydrodynamic model for simulating storm surge levels under historical and future climate conditions. Results show that the maximum storm surge heights of Maria and Dorian could increase by up to 0.31 m and 0.56 m, respectively. These results clearly show that future changes in storm surge heights are not negligible compared to end-of-the-century sea level rise projections, something that is sometimes overlooked in large-scale assessments of future coastal flood risk.

Modeling the potential impact of storm surge and sea level rise on coastal archaeological heritage: A case study from Georgia.

Climate change poses great risks to archaeological heritage, especially in coastal regions. Preparing to mitigate these challenges requires detailed and accurate assessments of how coastal heritage sites will be impacted by sea level rise (SLR) and storm surge, driven by increasingly severe storms in a warmer climate. However, inconsistency between modeled impacts of coastal erosion on archaeological sites and observed effects has thus far hindered our ability to accurately assess the vulnerability of sites. Modeling of coastal impacts has largely focused on medium-to-long term SLR, while observations of damage to sites have almost exclusively focused on the results of individual storm events. There is therefore a great need for desk-based modeling of the potential impacts of individual storm events to equip cultural heritage managers with the information they need to plan for and mitigate the impacts of storm surge in various future sea level scenarios. Here, we apply the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model to estimate the risks that storm surge events pose to archaeological sites along the coast of the US State of Georgia in four different SLR scenarios. Our results, shared with cultural heritage managers in the Georgia Historic Preservation Division to facilitate prioritization, documentation, and mitigation efforts, demonstrate that over 4200 archaeological sites in Georgia alone are at risk of inundation and erosion from hurricanes, more than ten times the number of sites that were previously estimated to be at risk by 2100 accounting for SLR alone. We hope that this work encourages necessary action toward conserving coastal physical cultural heritage in Georgia and beyond.

The Influence of Future Changes in Tidal Range, Storm Surge, and Mean Sea Level on the Emergence of Chronic Flooding

AbstractSea‐level rise is leading to increasingly frequent coastal floods globally. Recent research shows that changes in tidal properties and storm surge magnitudes can further exacerbate sea‐level rise‐related increases in flood frequencies. However, such non‐stationarity in tide and storm surge statistics are largely neglected in existing coastal flood projection methodologies. Here we develop a framework to explore the effect that different realizations of various sources of uncertainty have on projections of coastal flood frequencies, including changes in tidal range and storminess. Our projection methodology captures how observed flood rates depend on how storm surges coincide with tidal extremes. We show that higher flood rates and earlier emergence of chronic flooding are associated with larger sea‐level rise rates, lower flood thresholds, and increases in tidal range and skew surge magnitudes. Smaller sea‐level rise rates, higher flood thresholds and decreases in sea level variability lead to commensurately lower flood rates. Percentagewise, changes in tidal amplitudes generally have a much larger impact on flood frequencies than equivalent percentagewise changes in storm surge magnitudes. We explore several implications of these findings. Firstly, understanding future local changes in storm surges and tides is required to fully quantify future flood hazards. Secondly, existing hazard assessments may underestimate future flood rates as changes in tides are not considered. Finally, identifying the flood frequencies and severities relevant to local coastal managers is imperative to develop useable and policy‐relevant projections for decisionmakers.

Variability in surge levels in communities adjacent to the Houston Ship Channel industrial Corridor to changes in Hurricane characteristics

ABSTRACT The sensitivity of hurricane-driven storm surge in the Houston Ship Channel Industrial Corridor to variations in storm characteristics is studied. Hurricane surge from Hurricane Ike from 2008 was simulated using the Delft3D Flexible Mesh model. Use of the HURDAT2 wind parameter database allowed alteration of relevant hurricane characteristics to develop these alternate scenarios. Moving the actual landfall location of Ike toward the southwest at a distance of 50 km yielded a 123% increase at Manchester relative to observations during the storm. Relocation of the landfall location 200 km further southwest caused a 40% decrease in water level at the Galveston Bay entrance relative to observed values, but with a corresponding 50% water level increase at Manchester. Reduction of the storm forward speed to 25% of the actual speed over the nearshore led to a 25% increase in water level in Manchester over recorded values, while a 57% reduction of the storm forward speed over land increased water levels in Manchester by 30%. Incorporating river flow also identified locations where Galena Park’s residual floodwater is likely to remain after flooding. The results of this work can be used to guide modeling efforts of storm impacts on areas with narrow waterways.

A new surge index with the incorporation of cyclone track approach angle information for the Bay of Bengal

A novel tropical cyclone surge potential index (TCSPI) for estimating tropical cyclone (TC) induced potential peak surge levels near the landfall locations for the entire coast of the Bay of Bengal (BoB) is presented. The proposed TCSPI incorporates parameters that are often readily available in real-time for BoB: maximum sustained wind speed (Vmax), the radius of given wind (R), translation speed (S), approach angle (θ), coastal geometry (a, b), and bathymetry information (L30). The inclusion of approach angle and associated coastal geometry information led to improvements of the TCSPI to other existing surge indices. A retrospective analysis of TCSPI using the Indian Meteorological Department (IMD) and Joint Typhoon Warning Center (JTWC) tropical cyclone (TC) best track data from 1990 to 2021 for BoB suggests that the index captures historical events reasonably well. In particular, it explains up to ∼90% of observed surge variance and up to ∼92% of those peak surge values obtained from physics-based numerical simulation using the Advanced Circulation (ADCIRC) model. Further, we utilized renowned regression-based supervised machine learning (ML) models on small data samples. The Linear Regression (LR), Regression Tree (TR), Support Vector Machine (SVM), Ensembles of Trees (EnTR), and Gaussian Process Regression (GPR) models are incorporated that can rapidly predict peak surge height across an extensive coastal region of BoB, using the same input parameter information (i.e., Vmax, R, S,L30, and θ) that have been considered in the TCSPI at the landfall time frame. Finally, the observed peak surge values associated with historical TC events in the BoB are compared with those estimated by TCSPI, ADCIRC, and ML models. The errors associated with TCSPI are comparatively less. The consideration of approach angle information in the TCSPI has improved the accuracy of the overall prediction of peak surge height associated with historical TCs by up to 17% and 19% for observed and ADCIRC-simulated peak surges, respectively. The development of such a prediction methodology can reduce the numerical computation requirements to improve the surge hazard maps, and hence, proper implementation based on surge risk is possible.