Surge Levels Research Articles

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

In the storm surge model, the wind drag coefficient Cd is a critical parameter that has a great influence on the forecast of the storm surge level. In the present study, the effect of various wind drag coefficient parameterizations on the storm surge level is investigated in the Bohai Sea, Yellow Sea and East China Sea for Typhoons 7203 and 7303. Firstly, the impacts of initial values of a and b in the linear expression Cd = (a + b × U10) × 10−3 on the pure storm surge model are evaluated based on the data assimilation method. Results indicate that when a and b (i.e., the wind drag coefficients given by Smith, Wu, Geernaert et al. and Mel et al.) are non-zeros, the performance of the model has little difference, and the result from Wu is slightly better. However, they are superior to the performance of the model adopting zero initial values. Then, we discuss the influences of diverse ways of calculating wind drag coefficients, which are inverted by data assimilation method (including both linear and constant Cd) and given in the form of linear formulas, on simulating pure storm surge level. They show that the data assimilation-based coefficients greatly exceed those of the ordinary coefficient formulas. Moreover, the wind drag coefficient in the linear form is a little better than that in constant form when the data assimilation method is used. Finally, the assessment of the impact of astronomical tides on the storm surge level is conducted, and the simulation demonstrates that the storm surge model, which has the combination of four constituents (M2, S2, K1, O1) and wind drag coefficient inverted by the data assimilation method with the linear Cd, exhibits the best performance.

Using Neural Networks to Predict Hurricane Storm Surge and to Assess the Sensitivity of Surge to Storm Characteristics

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

1040. Comparative Surge Data on SARS CoV-2 Variants in a Rural Community – A Virtual Hospital Experience

Abstract Background UNC Health Blue Ridge is a nonprofit community teaching hospital with 145 staffed beds. The COVID-19 pandemic has challenged rural hospitals like ours with high occupancy and periodic surges. UNC Health Blue Ridge COVID Virtual Hospital (CVH) created a home monitoring program for enrolled patients with severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) infection utilizing disease risk stratification and pulse oximeter readings to dictate nurse and clinician follow-up. We report raw data that compare surge levels in our community for the Alpha (B.1.1.7), Delta (B.1.617.2) and Omicron (B.1.1.529) surges. Methods From April 2020 to present, the CVH enrolled patients diagnosed with COVID-19 based on FDA approved PCR tests. For this abstract, we defined any surge as an outpatient CVH census of greater than 50 patients or an inpatient census of >10 patients. We defined the maximum intensity of the surge as >20% outpatient SARS CoV-2 positivity and a CVH census >100 patients or >20 inpatients with SARS CoV-2 infection. Results For the outpatient setting, days of surge and maximum intensity were 144 / 98 days for Alpha, 92 / 71 days for Delta, 74 / 47 days for Omicron, respectively. Average daily CVH admissions during surges were 17.7 for Alpha, 26.15 for Delta, and 27.15 for Omicron. Total emergency department (ED) and urgent care visits were 12,765 and 23,696 for Delta, and 9701 and 16102 for Omicron. In the inpatient setting, days of surge and maximum intensity days were 102/76 days for Delta and 78/48 days for Omicron. Our peak inpatient COVID-19 daily census was 51 and 50 patients for Delta and Omicron. Conclusion Our CVH has enrolled over 8700 patients, and our hospital has not been on diversion during the COVID-19 pandemic, unlike many surrounding hospital systems of similar size. More importantly, the CVH has helped create a sustainable model that gathers local data to aid predictive algorithms and facilitate proactive rather than reactive resource allocations. This virtual model can be adapted for multiple other health conditions in the outpatient setting to improve patient safety and quality care. Disclosures All Authors: No reported disclosures.

Association of soluble E-selectin and morning blood pressure surge in patients with essential hypertension

Background: Elevated soluble E-selectin which reflects a state of endothelial activation with subsequent vasoconstriction may elevate morning blood pressure (BP) surge. This study aimed to analyze soluble E-selectin serum concentrations in patients with essential hypertension against normotensive healthy individuals and to find a role of such molecule in the phenomenon of morning BP surge. Material and methods: In this case-control study, a 24-hour ambulatory blood pressure monitoring with recording of morning BP surge and serum soluble E-selectin levels were measured for a total of 90 patients (60 patients with essential hypertension and 30 normotensive subjects as a control). Results: Hypertensive patients had higher body mass index in comparison to control subjects (30.7 ± 2.3 kg/m 2 vs. 27 ± 3.2 kg/m 2 , p < 0.001). Serum uric acid levels were higher in hypertensive patients than control subjects (7.26 ± 2.60 mg/dL vs. 5.92 ± 1.15 mg/dL, p = 0.028). Hypertensive patients had higher left ventricular mass index (LVMI) (110.5 ± 19.2 g/m2 vs. 99.8 ± 9.5 g/m 2 , p = 0.001). Patients with essential hypertension have higher level of soluble E-selectin than normotensive participants (180.6 ± 96.1 vs . 75.9 ± 31.5 ng/mL, p < 0.001). Soluble E-selectin was positively correlated with morning BP surge (r = 0.696, p ≤ 0.001). Conclusion: Patients with essential hypertension have higher level of soluble E-selectin than normotensive. Soluble E-selectin was positively correlated with morning BP surge.

Changes in storm surges based on a bias-adjusted reconstruction dataset from 1900 to 2010

To increase our understanding of multidecadal and longer-term variations in storm surges around the world, we require long-term and trustworthy data. The quantile delta mapping method was used to rectify the statistical biases of storm surge levels using data-driven methodologies to produce high-quality data on long-term storm surge levels. The results showed that the bias-adjusted surge levels were better than those from the raw ERA-20C reconstruction. Bias-adjusted surge levels indicated that most tide gauges globally showed an overall positive trend from 1900 to 2010, and more extreme storm surges occurred after the 1960s. The El Niño-Southern Oscillation (ENSO) has been identified as one of the key climate patterns influencing storm surges globally, especially at tide gauges around the Pacific Ocean coast in North America, the northwestern Atlantic Ocean, and the Mediterranean Sea. At regions of the Mediterranean Sea, northwestern Atlantic, and Pacific in North America, the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were significantly negatively correlated with storm surge. In contrast, the storm surges along the coast of northern Europe were considerably positively correlated with the AO. Storm surges were positively correlated with the Atlantic Multidecadal Oscillation (AMO) at various tide gauges along the Mediterranean coasts, the northwestern Atlantic and Pacific coasts of North America.

Numerical modeling of hydrodynamics on an elevated residential structure from varied wave and surge conditions using OpenFOAM

A Computational Fluid Dynamic (CFD) model study of wave and structure interactions on an elevated residential building under various air gap and surge/wave conditions was performed using the olaFlow, an open-source program using the OpenFOAM (Open-source Fields Operation And Manipulation) platform. The numerical model results, including free surface elevation, wave velocity, and vertical pressures on the underside of the elevated structure, showed a good agreement with the measured time-series data from the 1:6 scale hydraulic experiment (Duncan et al., 2021). The numerical simulations were used to extend the physical model tests by computing the vertical distribution of the pressure and resulting wave-induced horizontal forces/pressures, which were not measured in the physical model studies. The simulated results indicate that the pattern of pressure distributions at the frontal face of the elevated structure was controlled by water depth and wave-breaking types (nonbreaking, breaking, and broken waves). The wave induced-vertical force on the elevated structure strongly depends on wave height and the air gap, which is a net elevation from the still water level to the bottom of the structure, but the horizontal force shows complicated patterns due to the varied surge levels (flow depth), wave heights and air gaps. The new dimensionless parameter, α′/h, comprised of the air gap, incident wave height, and flow depth, is introduced and utilized to predict the horizontal forces on the elevated structure.

Impact of sea-level rise on cultural resources in the Delaware Bay region, USA

The Delaware Bay coastal zone includes at least 1580 cultural heritage resource sites; 700 of them could be damaged or lost by the year 2100. Future projections of sea-level rise (SLR) and storm surges identified cultural resources at high risk for inundation by the years 2030, 2050, 2080, and 2100. The projections are aimed to be high-end estimates preparing for long-term planning and “worse-case” scenarios. Sites located along the riverbanks are facing the highest threats from SLR. Coastal sites will be more frequently impacted by storms as surge levels increase with SLR. Storm-surge simulations predicted a non-linear increase in surge levels, reaching greater heights along the New Jersey side of the Bay compared to the Delaware side. Coastal sites without a protective dune system, or a dune less than 2 m in elevation, should be considered at immediate risk from coastal flooding. Shoreline erosion, and more frequent storms combined with SLR will increase vulnerability of coastal sites in the future. All endangered sites cannot be protected, therefore the results of the study should be of particular interest to historic preservation planners, cultural resources managers, Indigenous community leaders, and scholars to prioritize resources, develop the most effective preservation strategies, and plans for reconnaissance, survey, or data recovery from threatened sites and landscapes.

Assessment of compound flooding through seamless linkage of coastal hydrodynamic and inland catchment models

Compound flooding refers to the complex interactions among oceanographic, inland (catchment) hydrological, and meteorological processes with anthropogenic factors such as land use changes due to urbanization. To-wit, the impact of higher tide levels, storm surge, and high intensity rainfall events over the inland catchment collectively may result in more extensive flooding than the individual processes acting separately. In the context of climate change and more frequent extreme weather events, coastal cities, particularly those in the ASEAN region, are increasingly vulnerable to compound flooding, yet there is no convenient and user-friendly modelling approach available that would enable the planning community and decision-makers to envision compound flooding as part of resiliency-oriented urban plan development. We addressed this gap by developing the3D Resiliency Visualisation Platform (3DRVP), within which linked features are established using Python scripts to seamless integrate a 2D mixed land use fluvial/pluvial catchment model (PCSWMM) with a 2D/3Dcoastal hydrodynamic model (Delft3D) to simulate the dynamics ofcompound floods for the assessment of coastal inundation. This platform aims to assist planners, urban design professionals, and engineers with a realistic visualization tool to picture the urban infrastructure planning alternatives as well as to facilitate the real-time operational decision-making and evacuation activation with flood control strategies. The integrated platform theory is developed first and the platform then is trialled for a developing coastal area in south Bangkok, Thailand. Similar to many cities of the global south, data availability to calibrate models is limited and as such we used a mixed methods approach to explore model accuracy. The Delft3D model was calibrated successfully using water level data from a nearby gauge in the Gulf of Thailand for Typhoon Linda. The catchment model (PCSWMM) was validated using observed flood areas as reported by the local municipality. A 100-year design rainstorm was subsequently modelled and linked with the Typhoon Linda surge levels with results indicating the combination of rainfall flooding and storm surge would increase the flooded area by 25.6% over the system components modelled individually.

Machine learning-based assessment of storm surge in the New York metropolitan area

Storm surge generated from low-probability high-consequence tropical cyclones is a major flood hazard to the New York metropolitan area and its assessment requires a large number of storm scenarios. High-fidelity hydrodynamic numerical simulations can predict surge levels from storm scenarios. However, an accurate prediction requires a relatively fine computational grid, which is computationally expensive, especially when including wave effects. Towards alleviating the computational burden, Machine Learning models are developed to determine long-term average recurrence of flood levels induced by tropical cyclones in the New York metropolitan area. The models are trained and verified using a data set generated from physics-based hydrodynamic simulations to predict peak storm surge height, defined as the maximum induced water level due to wind stresses on the water surface and wave setup, at four coastal sites. In the generated data set, the number of low probability high-level storm surges was much smaller than the number of high probability low-level storm surges. This resulted in an imbalanced data set, a challenge that is addressed and resolved in this study. The results show that return period curves generated based on storm surge predictions from machine learning models are in good agreement with curves generated from high-fidelity hydrodynamic simulations, with the advantage that the machine learning model results are obtained in a fraction of the computational time required to run the simulations.

The transitional states of a floating wind turbine during high levels of surge

With the deployment of floating offshore wind turbines, the effect of their wind- and wave-induced platform motion on the turbine’s performance and life is of concern and requires low-order models to develop appropriate control strategies. This work focusses on platform surge as one of the main additional degrees of freedom. The aerodynamic behaviour during the rotor’s transition into propeller state is explored by assessing the spanwise and rotor-integrated aerodynamic forces on the rotor of the NREL 5 MW turbine through a 3D URANS CFD simulation. The CFD findings are compared with a modified actuator disk (AD) theory and 2D Blade-Element Momentum (BEM) approach. In addition to propeller state, where both rotor torque and thrust are negative, two other states were observed with opposite signs of torque and thrust: a braking state, with negative torque but positive thrust, and the second a quasi-windmill state with thrust negative yet torque positive. The results demonstrate that BEM coupled with AD can reliably predict the transitions to these states during such high levels of platform surge and give hints to how the underpinning force balances are affected by temporal perturbations.

The temporal clustering of storm surge, wave height, and high sea level exceedances around the UK coastline

The temporal clustering of storms presents consecutive storm surge and wave hazards that can lead to amplified flood and erosional damages; thus, clustering is important for coastal stakeholders to consider. We analyse the prevalence of storm clustering around the UK coastline by examining the temporal and spatial characteristics of storm surge, wave height, and high still sea level exceedances at the 1 in 1- and 5-year return levels. First, at the interannual timescale, we show that there are periods of high/low exceedance counts on national and regional scales. Elevated annual counts of exceedances with smaller magnitudes can occur without a respective signal of higher-magnitude exceedances. Secondly, at the intra-annual timescale, we show that high proportions of exceedances are clustering over short timescales. Storm surge, wave height and still sea level exceedances occurring < 50 days after the prior exceedance at a given site account for between ~ 35–44% and ~ 15–22% of all exceedances at the 1 in 1- and 5-year return levels, respectively. Still sea levels have the highest proportion of exceedances clustered in quick succession, with ~ 25% of 1 in 1-year exceedances occurring < 2 days after the previous at the same site. Spatially, for UK storm surges and still sea levels, the North Sea has the lowest proportion of clustering, whereas the North Atlantic and Bristol Channel have the highest. For English wave records, the highest proportions of clustering are found in the North Sea for exceedances of a lower magnitude and the English Channel for exceedances of a higher magnitude. These findings illuminate the prevalence of the clustering of coastal hazards around the UK—helping coastal stakeholders evaluate the threat of surges, waves, and sea levels clustering over short periods.

Therapeutic Investigation of Standardized Aqueous Methanolic Extract of Bitter Melon (Momordica charantia L.) for Its Potential against Polycystic Ovarian Syndrome in Experimental Animals' Model: In Vitro and In Vivo Studies.

Polycystic ovarian syndrome (PCOS) is an heterogenous, endocrine, metabolic, and multidisciplinary disorder of reproductive-aged females that aggravates insulin resistance, hyperandrogenism, obesity, menstrual irregularities, and infertility. Bitter melon is consumed as vegetable in various parts of the world. The purpose of this study was to provide the rationale for the folkloric uses of bitter melon (Momordica charantia L.) in reproductive abnormalities. HPLC analysis of standardized aqueous methanolic extract of bitter melon revealed the presence of various phytochemicals such as quercetin, gallic acid, benzoic acid, chlorogenic acid, syringic acid, p-coumaric acid, ferulic acid, and cinnamic acid. Twenty-five Swiss albino adult female rats (120–130 g) were acquired and divided into two groups (5 + 20). Letrozole (1 mg/kg p.o.) was used for four weeks to induce PCOS in twenty rats. Disease induction was confirmed by vaginal smear cytology analysis under the microscope. Animals were further divided into four groups, with one group as PCOS group, and the remaining three are treated with standardized extract of bitter melon (500 mg/kg p.o.), bitter melon plus metformin (500 mg/kg p.o.), and metformin alone for the period of next four weeks. After four weeks, the rats were euthanized at diestrus stage. Ovaries of the experimental animals were removed and fixed in 10% buffered formalin, and blood samples were obtained from direct cardiac puncture and stored. Ovaries histopathological analysis showed cystic follicles (9–10) in PCOS group, while, in all the treatment groups, we found developing and mature follicles. Similarly, hormone analysis showed significant (p < 0.001) reduction of LH surge, insulin, and testosterone levels and improvement in FSH levels. Lipid profile and antioxidant enzymes status were also significantly (p < 0.001) improved. In conclusion, the study validates the bitter melon potential as an insulin sensitizer and ovulation enhancer and authenticates its potential in PCOS management.

Improvement of empirical formulas to estimate the reduction effects by berms on irregular wave runup over a dune-berm coast

This study formulates the reduction effects of a sandy berm on irregular wave runup over a dune-berm coast. The numerical experiments by Park and Cox (2016) are closely re-examined to develop an empirical formula describing the variability of reduction effects of a sandy berm over a broad range of conditions. Based on a sequence of regression analyses, the reduction effects are expressed as a reduction factor in terms of normalized berm width, normalized surge level, and wave steepness in deep water. The comparison with the numerical experiments demonstrates that the regression formula can satisfactorily reproduce the variability of the reduction effects over the range of numerical experiments. The analysis of prediction uncertainty demonstrated that the derived formula reproduced the reduction effects observed in the numerical experiments with negligible bias and a 90% confidence interval of approximately ±20% relative error. In addition, conversion formulas between representative runup values based on different statistical definitions are derived to enable consistent comparisons between them. The proposed reduction formula is implemented into three empirical runup models that are applicable to the quick estimations of irregular wave runup on a dune-berm coast: the models by Park and Cox (2016), Stockdon et al. (2006), and Mase et al. (2013). Consistent comparisons were conducted among the empirical predictions and numerical experiments based on the statistical conversion formulas. Combined with the proposed reduction formula, all three models well reproduced the normalized 2% runup in the numerical experiments over a wide range of conditions. On the other hand, the uncertainty in the runup prediction appeared in different forms depending on the selected model. When the proposed reduction formula was implemented in the modified Park and Cox (2016) and modified Stockdon et al. (2006) models, the uncertainty was described by a log-normal distribution of the error ratio between the empirical predictions and numerical experiments. Quantitatively, these two models predicted 90% of the normalized runup on a dune within a range of relative error of less than approximately 20–30%. When the proposed reduction formula was combined with the model by Mase et al. (2013), the uncertainty followed a normal distribution of the residual error between the empirical predictions and numerical experiments. On the normalized runup, the model prediction indicated a small conservative bias (+0.05) and a root-mean-square error of 0.13.

Tridax procumbens Ameliorates Streptozotocin-Induced Diabetic Neuropathy in Rats via Modulating Angiogenic, Inflammatory, and Oxidative Pathways.

Tridax procumbens (TP) is a traditional Indian therapeutic plant and was evaluated for its blood glucose lowering abilities, as well as for its ability to curb diabetic neuropathy (DN). Administrating 45 mg/kg body weight of streptozotocin (STZ) intraperitoneally for four weeks, DN was induced in Wistar rats. After the rats' tails were clipped, the blood glucose levels were measured. Body weight and urine volume were also assessed. Oxidative stress makers such as superoxide dismutase (SOD), thiobarbituric acid reactive substances (TBARS), catalase (CAT), inflammatory cytokines for instance tumor necrosis factor (TNF)-α, and interleukin (IL)-1β were estimated. Further, protein kinase C (PKC-β) and vascular endothelial growth factor (VEGF) were also estimated as angiogenic markers. Behavioral parameters were also evaluated by using cold allodynia using acetone test, hot allodynia using Eddy's hot plate, grip strength test using Rota rod, and hyperalgesia test using Tail flick technique. The statistical assessment of findings was done employing one-way (ANOVA) analysis of variance, and subsequently Turkey as post hoc with GraphPad Prism software package. The ingestion of TP for 1 month in DN rats stemmed in a substantial decline in blood glucose concentrations matched to nontreated rats with DN. There had been a considerable improvement in DN as evident from the finding from biochemical markers. The serum level of antioxidant defense enzymes was significantly increased, while the activities of TBARS had been substantially reduced in the TP treated rats with DN. TP averted DN-triggered surge levels of TNF-α and IL-6 in the serum. Further, PKC-β and VEGF concentrations had been also reduced by the treatment TP. The findings of this research demonstrated that the restorative impact of TP on DN rats might be linked to the anti-inflammatory and antioxidative antiangiogenic retorts.

Air-sea interaction processes during hurricane Sandy: Coupled WRF-FVCOM model simulations

A fully-coupled atmospheric-ocean model was developed by coupling WRF (Weather Research and Forecasting Model) with FVCOM (the unstructured-grid, Finite-Volume Community Ocean Model) through the Earth System Model Framework (ESMF). The coupled WRF-FVCOM is configured with either hydrostatic or non-hydrostatic oceanic dynamics and can run with wave-current interactions. We applied this model to simulate the 2012 Hurricane Sandy in the western Atlantic Ocean. The experiments examined the impact of air-sea interactions on Sandy’s intensity/path and oceanic responses under hydrostatic and non-hydrostatic conditions. The results showed that the increased storm wind rapidly deepened the mixed layer depth when ocean processes were included. Intense vertical mixing brought cold water in the deep ocean towards the surface, producing a cold wake within the maximum wind zone underneath the storm. This process led to a sizeable latent heat loss from the ocean within the storm, and hence rapid air temperature and vapor mixing ratio drop above the sea surface. The storm intensified as the central sea-level pressure dropped. Improving air pressure simulation with ocean processes tended to reduce the storm size and strengthen its intensity, providing a better simulation of hurricane path and landfall. Turning on the non-hydrostatic process slightly improved the hurricane central sea-level pressure simulation and intensified the winds on the right side of the hurricane center. Hydrostatic and non-hydrostatic coupled WRF-FVCOMs captured Sandy-induced rapidly-varying flow over the shelf and the wind-induced surge level at the coast. The coupled models predicted a higher water elevation around the coastal areas where Sandy made landfall than the uncoupled model. The uncoupled and coupled models both showed more significant oceanic responses on the right side of the hurricane center, with a maximum during the Sandy crossing period when the clockwise-rotating frequency of Sandy wind was close to the local inertial frequency. The area with a maximum response varied with Sandy’s translation speed, more prominent in the deep region than over the slope, and more substantial under the non-hydrostatic condition. The simulated ocean responses agreed with the theoretical work of Price (1981). The nonhydrostatic experiments suggest that to resolve a fully storm-induced convection process, the oceanic model grid configuration should meet the O(1) criterion for the ratio of local water depth to the model horizontal resolution.

Neurosurgery Subspecialty Practice During a Pandemic: A Multicenter Analysis of Operative Practice in 7 U.S. Neurosurgery Departments During Coronavirus Disease 2019

ObjectiveChanges to neurosurgical practices during the coronavirus disease 2019 (COVID-19) pandemic have not been thoroughly analyzed. We report the effects of operative restrictions imposed under variable local COVID-19 infection rates and health care policies using a retrospective multicenter cohort study and highlight shifts in operative volumes and subspecialty practice.MethodsSeven academic neurosurgery departments' neurosurgical case logs were collected; procedures in April 2020 (COVID-19 surge) and April 2019 (historical control) were analyzed overall and by 6 subspecialties. Patient acuity, surgical scheduling policies, and local surge levels were assessed.ResultsOperative volume during the COVID-19 surge decreased 58.5% from the previous year (602 vs. 1449, P = 0.001). COVID-19 infection rates within departments' counties correlated with decreased operative volume (r = 0.695, P = 0.04) and increased patient categorical acuity (P = 0.001). Spine procedure volume decreased by 63.9% (220 vs. 609, P = 0.002), for a significantly smaller proportion of overall practice during the COVID-19 surge (36.5%) versus the control period (42.0%) (P = 0.02). Vascular volume decreased by 39.5% (72 vs. 119, P = 0.01) but increased as a percentage of caseload (8.2% in 2019 vs. 12.0% in 2020, P = 0.04). Neuro-oncology procedure volume decreased by 45.5% (174 vs. 318, P = 0.04) but maintained a consistent proportion of all neurosurgeries (28.9% in 2020 vs. 21.9% in 2019, P = 0.09). Functional neurosurgery volume, which declined by 81.4% (41 vs. 220, P = 0.008), represented only 6.8% of cases during the pandemic versus 15.2% in 2019 (P = 0.02).ConclusionsOperative restrictions during the COVID-19 surge led to distinct shifts in neurosurgical practice, and local infective burden played a significant role in operative volume and patient acuity.

A Flood Risk Management Model to Identify Optimal Defence Policies in Coastal Areas Considering Uncertainties in Climate Projections

Coastal areas are particularly vulnerable to flooding from heavy rainfall, sea storm surge, or a combination of the two. Recent studies project higher intensity and frequency of heavy rains, and progressive sea level rise continuing over the next decades. Pre-emptive and optimal flood defense policies that adaptively address climate change are needed. However, future climate projections have significant uncertainty due to multiple factors: (a) future CO2 emission scenarios; (b) uncertainties in climate modelling; (c) discount factor changes due to market fluctuations; (d) uncertain migration and population growth dynamics. Here, a methodology is proposed to identify the optimal design and timing of flood defense structures in which uncertainties in 21st century climate projections are explicitly considered probabilistically. A multi-objective optimization model is developed to minimize both the cost of the flood defence infrastructure system and the flooding hydraulic risk expressed by Expected Annual Damage (EAD). The decision variables of the multi-objective optimization problem are the size of defence system and the timing of implementation. The model accounts for the joint probability density functions of extreme rainfall, storm surge and sea level rise, as well as the damages, which are determined dynamically by the defence system state considering the probability and consequences of system failure, using a water depth–damage curve related to the land use (Corine Land Cover); water depth due to flooding are calculated by hydraulic model. A new dominant sorting genetic algorithm (NSGAII) is used to solve the multi-objective problem optimization. A case study is presented for the Pontina Plain (Lazio Italy), a coastal region, originally a swamp reclaimed about a hundred years ago, that is rich in urban centers and farms. A set of optimal adaptation policies, quantifying size and timing of flood defence constructions for different climate scenarios and belonging to the Pareto curve obtained by the NSGAII are identified for such a case study to mitigate the risk of flooding and to aid decision makers.

Estimating Compounding Storm Surge and Sea Level Rise Effects and Bias Correction Impact when Projecting Future Impact on Volcanic Islands in Oceania. Case Study of Viti Levu, Fiji

Oceania comprises many Small Island Developing States (SIDS), the majority of which are founded on volcanic islands. Small islands are generally vulnerable to the effects of climate change. However, a high number of islands and different coastal morphology make it challenging to accurately estimate climate change impact on this region. Nevertheless, quantifying hazards and thus assessing vulnerability is crucial for policymaking and adaptation efforts regarding SIDS. Meanwhile, Viti Levu is the principal island of Fiji. Therefore, climate change projection in Viti Levu helps estimate how volcanic islands in Oceania will be affected under future climate. This study projects the compound impact of storm surge by tropical cyclone (TC) and SLR on Viti Levu under current and future climate conditions. The primary goal of this study is to estimate the impact of extreme 50- and 100-years return storms on coastal areas and populations. This study also assesses the impact of the bias correction of TC intensity for impact assessment. Even though limited to one island, the results could facilitate the application on other volcanic islands, primarily in Melanesia. Even though Viti Levu is a high island, tropical cyclones can sustain extensive economic damage and result in high numbers of the temporarily displaced population in some low-lying coastal locations. The results show that bias can be significant when comparing observed and estimated datasets, particularly for less intense and future extreme events.

The contribution of hurricane remote ocean forcing to storm surge along the Southeastern U.S. coast

The dynamics controlling the spatial and temporal expressions of storm surges over the coastal wetlands and communities of the South Atlantic Bight (SAB) is complex and not well understood. Leveraging a newly developed high-density hyper-local network of water level sensors in the North Georgia coast, we implement and test an unstructured numerical coastal ocean model (up to 10-m horizontal resolution) that can resolve and diagnose the storm-induced sea-level rise during the two Hurricanes Matthew (2016) and Dorian (2019) that have shore-parallel tracks. Using a set of model sensitivity analyses we decompose the drivers of the storm surge into a component that is associated with direct surface forcing by the hurricanes over the targeted area (e.g., local atmospheric wind and pressure condition in the nested model domain) and remote ocean forcing that is connected to hurricane-induced sea level anomalies and baroclinic effect through the open boundary of the model. For both hurricanes, we find that local surface atmospheric forcing leads to a uniform alongshore response in water level along the entire North Georgia coast with amplitudes that are proportional to how close to shore are the hurricane tracks (e.g., stronger in Matthew and weaker in Dorian). However, the alongshore structure and location of maximum storm surge are determined entirely by the arrival timing of ocean remote forcing. In the case of Matthew, the remote forcing arrives within 2 h of the direct passage of the hurricane over North Georgia and drives peak surges in the northern region of the domain (e.g., the City of Savannah and Tybee Island). In contrast, during Dorian, there is a 14-h difference between the remote and local forcing, and maximum storm surges are found in the southern region around Sapelo Island. We estimate that if local and remote forcing were to be simultaneous, the peak storm surge and the water level would be amplified by up to 30% for Matthew and 50% for Dorian. While this sensitivity analysis only includes two hurricanes and is focused on a case study around North Georgia, it is clear that predicting and understanding the regional expressions and timing of the hurricanes' coastal-wide remote ocean forcing in the SAB is important for estimating worst-case scenarios for coastal communities as they face emergency and management decisions. • Newly developed high-resolution observation and model reveal the hidden role of extreme water level drivers. • The timing and structure of the remote ocean forcing exerts a dominant control on the regional distribution of storm surge. • The alignment in the peak timing of the local and remote forcing can increase the peak surge level by ∼50%.

A GIS-based Screening Workflow for Coastal Storm Surge Impact Assessments and Mitigation Action Consideration

Balstrøm, T. and Kirby, J., 2022. A GIS-based screening workflow for coastal storm surge impact assessments and mitigation action consideration. Journal of Coastal Research, 38(4), 712–724. Coconut Creek (Florida), ISSN 0749-0208. A GIS-based screening workflow is presented, providing first overviews of storm surge–induced inundation impacts along coastlines and where to consider mitigative action. The modeling is based on analysis of a hydro-conditioned digital terrain model (DTM) in Esri's ArcGIS Pro environment targeting undergraduate students, professional municipality planners, federal agencies, and more. Unlike existing commercial inundation modeling software, this application works in a simplified timeless setting on steady-state surge levels. This is accomplished only at the cost of not predicting when a specific targeted location is inundated. In return, fast computations are obtained. First, it is demonstrated how the DTM is turned into a hydro-conditioned terrain model, DHyMSea, protecting surges from entering land where sluices, high-water flaps, etc., are present. Second, the inundation screening is initiated for a user-defined number of sea levels. The outputs are individual raster layers for each level showing inundation extents and depths. Also, outputs are produced for each modeled level to back trace where the inundation came from. Third, a combined raster shows the minimum surge level to inundate any cell in the DHyMSea. Fourth, when considered where to raise protecting dikes, they can be digitized and have their elevation values stamped onto the DHyMSea. Finally, the inundation scenarios can be re-executed to evaluate the mitigation impact; if necessary, the dikes' locations and elevation properties can be reconsidered, and the workflow re-executed once more. The workflow has proven successful in first assessments of inundation threats where sea levels are expected to rise significantly over the coming decades. Additionally, the workflow has proven successful in quality assessments of a DTM. To demonstrate the workflow's capabilities, storm surge consequences for the greater Copenhagen region's coastline in Denmark are discussed for inundation scenarios ranging from 1.0 m to 3.9 m above sea level in steps of 0.1 m.