A geospatial framework to estimate depth of scour under buildings due to storm surge in coastal areas

Hurricanes and tropical storms represent one of the major hazards in coastal communities. Storm surge generated by strong winds and low pressure from these systems have the potential to bring extensive flooding in coastal areas. In many cases, the damage caused by the storm surge may exceed the damage from the wind resulting in the total collapse of buildings. Therefore, in coastal areas, one of the sources for major structural damage could be due to scour, where the soil below the building that serves as the foundation is swept away by the movement of the water. The existing methodologies to forecast hurricane flood damage do not differentiate between the different damage mechanisms (e.g., inundation vs. scour). Currently, there are no tools available that predominantly focus on forecasting scour-related damage for buildings. Such a tool could provide significant advantages for planning and/or preparing emergency responses. Therefore, the focus of this study was to develop a methodology to predict possible scour depth due to hurricane storm surges using an automated ArcGIS tool that incorporates the expected hurricane conditions (flow depth, velocity, and flood duration), site-specific building information, and the associated soil types for the foundation. A case study from Monmouth County (NJ), where the scour damages from 2012 Hurricane Sandy were recorded after the storm, was used to evaluate the accuracy of the developed forecasting tool and to relate the scour depth to potential scour damage. The results indicate that the developed tool provides relatively consistent results with the field observations.     

Hurricane Katrina storm surge delineation: implications for future storm surge forecasts and warnings

The storm surge in coastal Mississippi caused by Hurricane Katrina was unprecedented in the region. The height and geographic extent of the storm surge came as a surprise to many and exceeded pre-impact surge scenarios based on SLOSH models that were the basis for emergency preparedness and local land use decision-making. This paper explores the spatial accuracy of three interpolated storm surge surfaces derived from post-event reconnaissance data by comparing the interpolation results to a specific SLOSH run. The findings are used to suggest improvements in the calibration of existing pre-event storm surge models such as SLOSH. Finally, the paper provides some suggestions on an optimal surge forecast map that could enhance the communication of storm surge risks to the public.     

Building with Nature: in search of resilient storm surge protection strategies

Low-lying, densely populated coastal areas worldwide are under threat, requiring coastal managers to develop new strategies to cope with land subsidence, sea-level rise and the increasing risk of storm-surge-induced floods. Traditional engineering approaches optimizing for safety are often suboptimal with respect to other functions and are neither resilient nor sustainable. Densely populated deltas in particular need more resilient solutions that are robust, sustainable, adaptable, multifunctional and yet economically feasible. Innovative concepts such as ‘Building with Nature’ provide a basis for coastal protection strategies that are able to follow gradual changes in climate and other environmental conditions, while maintaining flood safety, ecological values and socio-economic functions. This paper presents a conceptual framework for Building with Nature that is used to evaluate coastal protection strategies, based on a case study of the Holland coast in the Netherlands. The added value and the limitations of these strategies are discussed.     

Building with Nature: in search of resilient storm surge protection strategies

Low-lying, densely populated coastal areas worldwide are under threat, requiring coastal managers to develop new strategies to cope with land subsidence, sea-level rise and the increasing risk of storm-surge-induced floods. Traditional engineering approaches optimizing for safety are often suboptimal with respect to other functions and are neither resilient nor sustainable. Densely populated deltas in particular need more resilient solutions that are robust, sustainable, adaptable, multifunctional and yet economically feasible. Innovative concepts such as ??Building with Nature?? provide a basis for coastal protection strategies that are able to follow gradual changes in climate and other environmental conditions, while maintaining flood safety, ecological values and socio-economic functions. This paper presents a conceptual framework for Building with Nature that is used to evaluate coastal protection strategies, based on a case study of the Holland coast in the Netherlands. The added value and the limitations of these strategies are discussed.     

Numerical storm surge model for India and Pakistan

The northeastern sector of the Arabian Sea, which covers the Gujarat coast of India and western coast of Pakistan, is a region vulnerable to extreme sea levels associated with tropical cyclones (TCs). Although the frequency of tropical cyclones in the Arabian Sea is not high, the coastal regions of India and Pakistan suffer in terms of loss of life and property caused by the surges. In view of this a location-specific fine resolution model is developed for the Gujarat coast of India and adjoining Pakistan coast. The east–west and north–south grid distance is about 3.0 km. Using this model, numerical experiments are carried out to simulate the surges generated by 1999 and 2001 cyclones which struck the Pakistan coast. The model computed surges are in agreement with the available observational estimates.     

Integrating Monte Carlo and hydrodynamic models for estimating extreme water levels by storm surge in Colombo,Sri Lanka

The prediction of high extremes in sustained water level is very important for coastal engineering design and planning. The recorded historical water level datasets in Colombo, Sri Lanka, are not long enough for the traditional frequency analysis in predicting extreme water levels, such as 50-, 100- and 200-year extreme water levels. In this study, the integrated ADCIRC + SWAN hydrodynamic model and Monte Carlo model have been applied to predict extreme water level in Colombo station of Sri Lanka. The meteorological driving forces of cyclone storm surge are simulated by Monte Carlo stochastic model. The calibrated ADCIRC model with SWAN wave model is used to simulate the potential surge setups with the driving forces generated by Monte Carlo model. By ranking the maximum high water levels in each storm surge procedure, the estimation on extreme high water levels for the desired return period is proposed in this study. The estimated extreme high water levels with return period of 50, 100 and 200 years are 1.28, 1.40 and 1.50 m correspondingly. The estimated extreme high water levels are recommended for engineering design and planning.     

Inclusion of ice cover in a storm surge model for the Beaufort Sea

Storm surges in the Beaufort Sea present a severe problem for navigation as well as for offshore oil drilling activities. Influence of ice cover on storm surges in the Beaufort Sea is examined making use of a numerical model as well as a set of observations.The automated shallow-water model of Henry has been modified to incorporate ice cover and is adapted to the Beaufort Sea. The leading edge of the permanent ice is calculated from the loci of identifiable points. Generalized similarity theory is employed to compute wind stresses. Simulations are made using model-predicted ice concentrations and observed ice concentrations. Ice motion is relatively small in units of model grid distance (approximately 18 km) during surges. Spherical effects are important and should be included in future adaptations of the model. Comparison of the computed surges with observed surges for eight different events showed reasonable agreement.     

Inclusion of ice cover in a storm surge model for the Beaufort Sea

Storm surges in the Beaufort Sea present a severe problem for navigation as well as for offshore oil drilling activities. Influence of ice cover on storm surges in the Beaufort Sea is examined making use of a numerical model as well as a set of observations. The automated shallow-water model of Henry has been modified to incorporate ice cover and is adapted to the Beaufort Sea. The leading edge of the permanent ice is calculated from the loci of identifiable points. Generalized similarity theory is employed to compute wind stresses. Simulations are made using model-predicted ice concentrations and observed ice concentrations. Ice motion is relatively small in units of model grid distance (approximately 18 km) during surges. Spherical effects are important and should be included in future adaptations of the model. Comparison of the computed surges with observed surges for eight different events showed reasonable agreement.     

Simulation of the Hurricane Dennis storm surge and considerations for vertical resolution

A high-resolution storm surge model of Apalachee Bay in the northeastern Gulf of Mexico is developed using an unstructured grid finite-volume coastal ocean model (FVCOM). The model is applied to the case of Hurricane Dennis (July 2005). This storm caused underpredicted severe flooding of the Apalachee Bay coastal area and upriver inland communities. Accurate resolution of complicated geometry of the coastal region and waterways in the model reveals processes responsible for the unanticipated high storm tide in the area. Model results are validated with available observations of the storm tide. Model experiments suggest that during Dennis, excessive flooding in the coastal zone and the town of St. Marks, located up the St. Marks River, was caused by additive effects of coincident high tides (~10–15% of the total sea-level rise) and a propagating shelf wave (~30%) that added to the locally wind-generated surge. Wave setup, the biggest uncertainty, is estimated on the basis of empirical and analytical relations. The Dennis case is then used to test the sensitivity of the model solution to vertical discretization. A suite of model experiments is performed with varying numbers of vertical sigma (σ) levels, with different distribution of σ-levels within the water column and a varying bottom drag coefficient. The major finding is that the storm surge solution is more sensitive to resolution within the velocity shear zone at mid-depths compared to resolution of the upper and bottom layer or values of the bottom drag coefficient.     

Parameter estimations of a storm surge model using a genetic algorithm

A genetic algorithm was used to optimize the parameters of the two-dimensional Storm Surge/Tide Operational Model (STORM) to improve sea level predictions of storm surges. The model was then tested using data from Typhoon Maemi, which landed on the Korean Peninsula in 2003. The following model parameters were used: the coefficients for bottom drag, background horizontal diffusivity, Smagorinsky’s horizontal viscosity, and sea level pressure scaling. The simulation results using the optimized parameters improved sea level predictions. This study demonstrates that parameter optimizations and their adequate applications are essential for improving model performance.     

Advances in surrogate modeling for storm surge prediction: storm selection and addressing characteristics related to climate change

This paper establishes various advancements for the application of surrogate modeling techniques for storm surge prediction utilizing an existing database of high-fidelity, synthetic storms (tropical cyclones). Kriging, also known as Gaussian process regression, is specifically chosen as the surrogate model in this study. Emphasis is first placed on the storm selection for developing the database of synthetic storms. An adaptive, sequential selection is examined here that iteratively identifies the storm (or multiple storms) that is expected to provide the greatest enhancement of the prediction accuracy when that storm is added into the already available database. Appropriate error statistics are discussed for assessing convergence of this iterative selection, and its performance is compared to the joint probability method with optimal sampling, utilizing the required number of synthetic storms to achieve the same level of accuracy as comparison metric. The impact on risk estimation is also examined. The discussion then moves to adjustments of the surrogate modeling framework to support two implementation issues that might become more relevant due to climate change considerations: future storm intensification and sea level rise (SLR). For storm intensification, the use of the surrogate model for prediction extrapolation is examined. Tuning of the surrogate model characteristics using cross-validation techniques and modification of the tuning to prioritize storms with specific characteristics are proposed, whereas an augmentation of the database with new/additional storms is also considered. With respect to SLR, the recently developed database for the US Army Corps of Engineers’ North Atlantic Comprehensive Coastal Study is exploited to demonstrate how surrogate modeling can support predictions that include SLR considerations.     

On the accuracy of atmospheric forcing for extra-tropical storm surge prediction

The ability of the SMARA storm surge numerical prediction system to reproduce local effects in estuarine and coastal winds was recently improved by considering one-way coupling of the air–sea momentum exchange through the wave stress, and best forecasting practices for downscaling. The inclusion of long period atmospheric pressure forcing in tide and tide/surge calculations corrected a systematic error in the surge, produced by the South Atlantic Ocean quasi-stationary pressure patterns. The maximum forecast range for the storm surge at Buenos Aires provided by the real-time use of water level observations is approximately 12 h. The best available water level prediction is the 6-h forecast (nowcast) based on the closest water level observations. The 24-h forecast from the numerical models slightly improves this nowcast. Although the numerical forecast accuracy degrades after the first 48 h, the improvement to the full range observation-based prediction is maintained at the inner Río de la Plata area and extends to the first 3 days at the intermediate navigation channels.     

Assessing future risk: quantifying the effects of sea level rise on storm surge risk for the southern shores of Long Island, New York

Sea level rise threatens to increase the impacts of future storms and hurricanes on coastal communities. However, many coastal hazard mitigation plans do not consider sea level rise when assessing storm surge risk. Here we apply a GIS-based approach to quantify potential changes in storm surge risk due to sea level rise on Long Island, New York. We demonstrate a method for combining hazard exposure and community vulnerability to spatially characterize risk for both present and future sea level conditions using commonly available national data sets. Our results show that sea level rise will likely increase risk in many coastal areas and will potentially create risk where it was not before. We find that even modest and probable sea level rise (.5 m by 2080) vastly increases the numbers of people (47% increase) and property loss (73% increase) impacted by storm surge. In addition, the resulting maps of hazard exposure and community vulnerability provide a clear and useful example of the visual representation of the spatial distribution of the components of risk that can be helpful for developing targeted hazard mitigation and climate change adaptation strategies. Our results suggest that coastal agencies tasked with managing storm surge risk must consider the effects of sea level rise if they are to ensure safe and sustainable coastal communities in the future.     

Assessing storm surge impacts on coastal inundation due to climate change: case studies of Baltimore and Dorchester County in Maryland

Natural Hazards - Hurricane Isabel (2003) generated record flooding around Chesapeake Bay and caused extensive damage in rural Eastern Shore of Maryland and metropolitan cities like Baltimore....     

The impact of a storm surge on business establishments in the Houston MSA

This paper examines the possible storm surge damage from a major hurricane to hit the Houston Metropolitan Statistical Area (MSA.) Using storm surge analysis on a unique data set compiled from the Texas Workforce Commission (QCEW), the paper estimates the expected industry-level damage for each county in the Houston MSA. The advantages of using GIS to analyze the expected storm surge damage estimation is that it provides an accurate estimation of the number of affected employees and probable wages losses, by industry and county, based on QCEW data. The results indicate that the ‘Basic Chemical Manufacturing’ and ‘Oil and Gas Extraction’ industries incur the highest employee and payroll losses while the ‘Restaurants and Eateries’ has the largest establishment damage if a major hurricane were to hit the Houston MSA.     

Extremity analysis of storm surge for fixing safe design water level

Phenomenal storm surge levels associated with cyclones are common in East Coast of India. The coastal regions of Andhra Pradesh are in rapid stride of myriad marine infrastructural developments. The safe elevations of coastal structures need a long-term assessment of storm surge conditions. Hence, past 50 years (1949–1998), tropical cyclones hit the Bay are obtained from Fleet Naval Meteorological & Oceanographic Center, USA, and analyzed to assess the storm surge experienced around Kakinada and along south Andhra Pradesh coast. In this paper, authors implemented Rankin Hydromet Vortex model and Bretschneider’s wind stress formulation to hindcast the surge levels. It is seen from the hindcast data that the November, 1977 cyclone has generated highest surge of the order of 1.98 m. Extreme value analysis is carried out using Weibull distribution for long-term prediction. The results reveal that the surge for 1 in 100-year return period is 2.0 m. Further the highest surge in 50 years generated by the severe cyclone (1977) is numerically simulated using hydrodynamic model of Mike-21. The simulation results show that the Krishnapatnam, Nizampatnam and south of Kakinada have experienced a surge of 1.0, 1.5 and 0.75 m, respectively.     

Simulating storm surge waves for structural vulnerability estimation and flood hazard mapping

Natural Hazards - Wave action during storm surge is a common cause of building damage and therefore a critical consideration when estimating structural vulnerability and mapping flood risk....