Storm Surge Research Articles

Quantifying future changes of flood hazards within the Broadland catchment in the UK

Flooding represents the greatest natural threat to the UK, presenting severe risk to populations along coastlines and floodplains through extreme tidal surge and hydrometeorological events. Climate change is projected to significantly elevate flood risk through increased severity and frequency of occurrences, which will be exacerbated by external drivers of risk such as property development and population growth throughout floodplains. This investigation explores the entire flood hazard modelling chain, utilising the nonparametric bias correction of UKCP18 regional climate projections, the distributed HBV-TYN hydrological model and HEC-RAS hydraulic model to assess future manifestation of flood hazard within the Broadland Catchment, UK. When assessing the independent impact of extreme river discharge and storm surge events as well as the impact of a compound event of the two along a high emission scenario, exponential increases in hazard extent over time were observed. The flood extent increases from 197 km2 in 1990 to 200 km2 in 2030, and 208 km2 in 2070. In parallel, exponential population exposure increases were found from 13,917 (1990) to 14,088 (2030) to 18,785 (2070). This methodology could see integration into policy-based flood risk management by use of the developed hazard modelling tool for future planning and suitability of existing infrastructure at a catchment scale.

Stability Analysis of Cofferdam with Double-Wall Steel Sheet Piles under Wave Action from Storm Surges

Double-wall steel sheet piles (DSSPs) are widely used in large-span cofferdams for docks due to their good performance against wave action during storm surges. This paper describes a study of the dynamic behavior of a DSSP cofferdam under wave action through flume tests and a numerical simulation that combined computational fluid dynamics (CFD) and the finite element method. The influences of the water level and wave height on the DSSP cofferdam were investigated experimentally and numerically. Tall waves in shallow water broke upon and impacted the seaside pile with large dynamic wave pressure, dramatically increasing the stress and displacement of the seaside pile. The overlap of the traveling and reflected waves increased the excess pore water pressure near the seaside pile due to taller overlapped waves and higher wave frequency. The DSSP cofferdam failed under the combined actions of the dynamic wave pressure and erosion of the landside seabed. The leakage and overflow of the breaking waves resulted in significant erosion of the landside seabed and greatly weakened the support of the seabed. The dynamic wave pressure then pushed the DSSP cofferdam until it failed. The simulation with the combined methods of CFD and FEM resulted in trends that were similar to those of the test measurements. Compared to the quasi-static method and pseudo-dynamic method, the results of the simulation via the present method were much closer to the test results because the simulation included the effects of breaking waves. The reinforced measure worked well to prevent the DSSP cofferdam in a sandy seabed foundation from continuous failures of deformation–leakage–erosion–tilting. However, it failed in a clay interlayer seabed foundation due to the large settlement.

Storm surge induced by 2019 Tropical Storm Pabuk and impact on the shoreline in Kuala Nerus Terengganu, east coast of Peninsular Malaysia

This study investigates the one-year shoreline impacts of 2019 Tropical Storm Pabuk and the morphological changes of beaches in the study area of Kuala Nerus in Malaysia mediated by the hydrodynamic conditions. The study used a numerical model that utilised the two-way coupling of the wave and hydrodynamic model of MIKE 21 to simulate the event. The impact of the event was determined by employing the Digital Shoreline Analysis System, along with regular in-situ measurements of beach profiles carried out monthly. The results show a significant retreat rate of −58 m/year at Tok Jembal, highlighting the significant erosive impact, particularly on the northwest side of the airport. Despite a storm surge height below the extreme thresholds (>1 m), the beach morphology and the presence of coastal protection structures exerted a significant modulating influence. Batu Rakit, which is characterised by a wide beach, exhibited increased wave run-up and erosion resulting from the combined effect of a tidal surge of 0.64 m and significant wave heights of more than 1.12 m. UMT and Teluk Ketapang, on the other hand, which are shielded by coastal structures and influenced by longshore currents, experienced beach accretion. The intensification of the recirculation current with the surge is postulated to have contributed to the pronounced erosion at Pengakalan Maras. Although the overall magnitude of the storm surge was not exceptional, this study emphasises the importance of studying the effects of storm surges on monsoonal coasts. The intricate interplay between waves and currents, elucidated by coupled hydrodynamic and wave modelling, is crucial for understanding the consequences of storms and for proactive coastal management strategies. This study emphasises the crucial role of detailed hydrodynamic modelling in developing effective erosion control measures, which is an ongoing challenge for coastal communities.

Storm surge modeling in the AI era: Using LSTM-based machine learning for enhancing forecasting accuracy

Physics simulation results of natural processes usually do not fully capture the real world. This is caused for instance by limits in what physical processes are simulated and to what accuracy. In this work we propose and analyze the use of an LSTM-based deep learning network machine learning (ML) architecture for capturing and predicting the behavior of the systemic error for storm tide forecast models with respect to real-world water elevation observations from gauge stations during hurricane events. The overall goal of this work is to predict the systemic error of the physics model and use it to improve the accuracy of the simulation results post factum (i.e., to correct the model bias). We trained our proposed ML model on a dataset of 61 historical storms in the coastal regions of the south and southeastern U.S. and we tested its performance in bias correcting modeled water level data predictions from Hurricane Ian (2022). We show that our model can consistently improve the forecasting accuracy for Hurricane Ian – unknown to the ML model – at the majority of gauge station coordinates. Moreover, by examining the impact of using different subsets of the initial training dataset, containing a number of relatively similar or different hurricanes in terms of hurricane track, we found that we can obtain similar quality of bias correction by only using a subset of six hurricanes. This is an important result that implies the possibility to apply a pre-trained ML model to real-time hurricane forecasting results with the goal of bias correcting and improving the forecast accuracy. The presented work is an important first step in creating a bias correction system for real-time storm tide forecasting applicable to the full simulation area. It also presents a highly transferable and operationally applicable methodology for improving the accuracy in a wide range of physics simulation scenarios beyond storm tide forecasting.

A new approach for the assessment of coastal flooding risk. Application in Rhodes island, Greece

A directional, copula-based, Monte Carlo approach is proposed for the quantitative assessment of a new definition of coastal flooding risk, namely a probability-based index due to extreme combinations of wave conditions and sea levels, which could be a key element for coastal zone planning and management. This approach differs from the standard practice, which focuses on the extreme events and studies either their specific return values or their worst-case scenarios. The adopted approach incorporates mean wave direction as a covariate into the probabilistic representation of sea states, and via the use of a variety of probabilistic and statistical tools, represents a total of five stochastic variables, namely the significant wave height, the peak wave period, the mean wave direction, the storm surge, and tide. Two directional, alternative methods have been developed; a trivariate copula- and a bivariate copula-based method, that seemed to produce quite similar results. Four types of copulas have been examined for their goodness-of-fit, i.e., three Archimedean copulas, namely, the Clayton, the Gumbel, and the Frank copulas, as well as the elliptical Gaussian copula. A comparative analysis of the two methods is performed aiming to point out their pros, as well as their accuracy and complexity issues. The application of the proposed method to the natural beaches of the Rhodes Island in Greece, in combination with a recently developed formula for wave run-up, highlighted the most crucial parameters affecting the coastal flooding risk in the study area.

Structural performance and fragility assessment of elevated residential buildings during 2017–2018 hurricanes in Texas and Florida

Elevating coastal houses on columns or piles is recognized as an effective technique for reducing the risks of flooding and storm surges. However, recent hurricanes have exposed a potential vulnerability in this approach, as many elevated residential structures sustained significant wind damage even at wind speeds below the current design level. Elevating a structure increases its exposure to stronger winds and leads to potential changes in the wind effects due to the air gap beneath the floor. However, there has been limited research comparing the performance of actual constructed elevated and slab-on-grade residential structures during realistic wind events. To fill this gap, authors utilized publicly available building performance datasets, collected by the Structural Extreme Events Reconnaissance (StEER) Network and others, to extract data on the performance of 851 elevated and 1536 slab-on-grade residential houses impacted by Hurricanes Irma, Harvey, and Michael. Effects of several key parameters were analyzed in terms of the intensity and distribution of the damage experienced by the different components. Furthermore, empirical fragility functions were developed for different roof shapes and cladding materials by combining the estimated wind speed that affected each structure and the assigned damage ratios for the roof and wall claddings. Analysis using the Wilcoxon rank-sum test demonstrated a statistically significant difference in the median wind speeds of various damage states of the components in both elevated and slab-on-grade houses. Additionally, a significant difference was observed in Texas slab-on-grade houses which experienced either no, minor, moderate, or severe damage, compared to their Florida counterparts. Results showed that elevated houses experience more damage than their slab-on-grade counterparts regardless of the roof shape, cladding materials, and construction age. Additionally, results showed that the elevated houses built in Texas exhibit the highest level of vulnerability among the various housing groups studied. Specifically, the percentage of elevated houses in Texas that experienced destroyed damage state surpassed those of slab-on-grade houses in Texas and elevated houses in Florida by 343 % and 166 %, respectively. The paper identified several knowledge gaps, and it emphasized the need for further experimental tests of building components and different building geometries to improve existing risk models.

Formulating a warning threshold for coastal compound flooding: A copula-based approach

To calculate warning thresholds for compound-flooding events triggered by heavy rainfall coupled with storm tides in Taiwan’s coastal urban areas, we applied copula-based analysis to observation data collected from 2001 till 2022 for Taipei City and New Taipei City and developed an empirical formula that accounts for both the capacity of the drainage infrastructure, which partially depends on the coastal sea level and varies over time, and the amount of precipitation. Compared against observation data from flood detectors, our predictions exhibited an accuracy of 85.2 % and 78.8 % for Taipei City and New Taipei City, respectively, thus improving upon the 62.8 % and 68.5 % success rates for thresholds estimated using only the hourly accumulated rainfall. These promising preliminary results suggest that reliable flood warnings for tidal-basin regions can be expedited by employing our formula and inputting rainfall and sea-level values from ensemble typhoon and storm-surge forecasts.

Improvement in Storm Surge Numerical Forecasting Based on Wave Buoys Data

The maximum wind speed radius of a strong typhoon making landfall is an important factor influencing the numerical forecasting of storm surges. A method for inverting the maximum wind speed radius of typhoons based on wave buoys data was designed to significantly reduce the error in 24 h storm surge forecasting in this paper, and an operation scheme was proposed to enhance the storm surge numerical forecasting system based on this method. Hangzhou Bay and the Yangtze River Estuary, which have been frequently impacted by typhoons over the past five years, were selected as the research area. Common schemes for the maximum wind speed radius were analyzed, and five ladder schemes (10, 15, 20, 25, and 30 km) were established for wave and storm surge numerical model verification of Typhoon Muifa in 2022. Based on a comparison of the wave hindcast results and wave buoys observation data, the wave hindcast result of the commonly used scheme (30 km) was significantly greater than that of the observation data, and the optimal scheme (15 km) closest to the observation data could be determined during the 48 h warning period. Moreover, it was difficult to identify the optimal scheme during the 48 h warning period based on the storm surge hindcast results. A 24 h storm surge numerical forecasting test was performed with the commonly used scheme (30 km) and the optimal scheme (15 km). The results showed that the root mean square error (RMSE) of the optimal scheme (15 km) was 34% lower than that of the commonly used scheme (30 km), while the maximum storm surge error was also reduced from 47.7% for the commonly adopted scheme (30 km) to 11.8% for the optimal scheme (15 km). The maximum storm surges under the optimal scheme (15 km) along Hangzhou Bay and the Yangtze River Estuary ranged from 1.9 to 2.2 m, which were closer to the observation data, and the maximum storm surge under the commonly used scheme (30 km) was 0.8~1.2 m greater than that under the optimal scheme (15 km).

Fingerprinting Mediterranean hurricanes using pre-event thermal drops in seawater temperature

Extreme atmospheric-marine events, known as medicanes (short for “Mediterranean hurricanes”), have affected the Mediterranean basin in recent years, resulting in extensive coastal flooding and storm surges, and have occasionally been responsible for several casualties. Considering that the development mechanism of these events is similar to tropical cyclones, it is plausible that these phenomena are strongly affected by sea surface temperatures (SSTs) during their development period (winter and autumn seasons). In this study, we compared satellite data and the numerical reanalysis of SSTs from 1969 to 2023 with in situ data from dataloggers installed at different depths off the coast of southeastern Sicily as well as from data available on Argo floats on the Mediterranean basin. A spectral analysis was performed using a continuous wavelet transform (CWT) for each SST time series to highlight the changes in SSTs prior to the occurrence of Mediterranean Hurricanes as well as the energy content of the various frequencies of the SST signal. The results revealed that decreases in SST occurred prior to the formation of each Mediterranean hurricane, and that this thermal drop phenomenon was not observed in intense extra-tropical systems. The spectral analyses revealed that high CWT coefficients representing high SST energy contents were observed before the occurrence of a Mediterranean hurricane. This information may provide a useful fingerprint for distinguishing Mediterranean hurricanes from common seasonal storms at the onset of these events.

An experimental study on the evolution of beach profiles under different beach nourishment methods

Due to the influence of storm surges, hard coastal protection structures may exacerbate beach erosion, hence beach nourishment methods are considered in coastal protection and restoration efforts. An experiment is conducted in a wave flume to compare the advantages and disadvantages of three types of beach nourishment methods (berm nourishment, profile nourishment, and bar nourishment) under non-storm and storm wave conditions. The experiment analyzes wave height distribution, beach morphology changes, net sediment transport, shoreline recession, beach width increase, and beach volume changes. The findings indicate that the implementation of bar nourishment strategies facilitates the pre-breaking of storm waves, thereby mitigating the disruption caused by wave breaking on water flow dynamics. Moreover, the application of berm and profile nourishment methods proves to be highly effective in expanding the width of the beach and bolstering its stability. However, it is worth noting that bar nourishment exhibits limited efficacy in terms of restoring beach berms and mitigating coastal erosion within the same duration of wave action. The study presents two equations incorporating hydrodynamic parameters to predict the erosion or accretion status of beaches, and compares the predicted results with experimental data, thereby refining the criteria and making the formulas more applicable to beach nourishment projects. These findings provide valuable insights into the strengths and weaknesses of different beach nourishment methods, which will contribute to the implementation of nourishment projects.

UAV Photogrammetry-based Sea Level Establishment for a Storm Surge Early Warning System in Wolpo-beach, South Korea

UAV Photogrammetry-based Sea Level Establishment for a Storm Surge Early Warning System in Wolpo-beach, South Korea

Simulation of the Evolution of Cross-Shore Beach Profiles using SBEACH Modelling

Coastal erosion significantly threatens sandy beaches worldwide, impacting coastal communities and ecosystems. This research investigates the application of SBEACH (Storm-induced BEAch Change), a numerical beach modelling computer programme, as a tool for coastal engineers to predict beach profile evolution in response to wave and tidal actions. The study used SBEACH simulations to explore the equilibrium beach profiles under varying conditions, including wave height, wave period, sediment grain size, and beach slope. This study also examined the effects of storm tides on equilibrium profiles and proposed protective sand dunes' dimensions to safeguard against storm surges. This research highlights SBEACH's potential as a valuable tool for assessing and planning coastal erosion mitigation strategies. While SBEACH provides insights into cross-shore beach profile dynamics, combining it with GENESIS offers a more comprehensive approach to coastal management. However, it is crucial to consider real-world engineering experience and safety factors when deciding on beach stabilisation and protection measures. Coastal countries can benefit from such modelling approaches to better manage and protect their sandy beaches, which play a crucial role in coastal defence and maintaining ecosystem integrity.

Mangroves as nature-based mitigation for ENSO-driven compound flood risks in a large river delta

Abstract. Densely populated coastal river deltas are very vulnerable to compound flood risks coming from both oceanic and riverine sources. Climate change may increase these compound flood risks due to sea level rise and intensifying precipitation events. Here, we investigate to what extent nature-based flood defence strategies, through the conservation of mangroves in a tropical river delta, can contribute to mitigate the oceanic and riverine components of compound flood risks. While current knowledge of estuarine compound flood risks is mostly focussed on short-term events such as storm surges (taking 1 or a few days), longer-term events, such as El Niño events (continuing for several weeks to months) along the Pacific coast of Latin America, are less studied. Here, we present a hydrodynamic modelling study of a large river delta in Ecuador aiming to elucidate the compound effects of El Niño-driven oceanic and riverine forcing on extreme high water level propagation through the delta and, in particular, the role of mangroves in reducing the compound high water levels. Our results show that the deltaic high water level anomalies are predominantly driven by the oceanic forcing but that the riverine forcing causes the anomalies to amplify upstream. Furthermore, mangroves in the delta attenuate part of the oceanic contribution to the high water level anomalies, with the attenuating effect increasing in the landward direction, while mangroves have a negligible effect on the riverine component. These findings show that mangrove conservation and restoration programmes can contribute to nature-based mitigation, especially the oceanic component of compound flood risks in a tropical river delta.

Tracking the spatial footprints of extreme storm surges around the coastline of the UK and Ireland

Storm surges are the most important driver of flooding in many coastal areas. Understanding the spatial extent of storm surge events has important financial and practical implications for flood risk management, reinsurance, infrastructure reliability and emergency response. In this paper, we apply a new tracking algorithm to a high-resolution surge hindcast (CODEC, 1980–2017) to characterize the spatial dependence and temporal evolution of extreme surge events along the coastline of the UK and Ireland. We quantify the severity of each spatial event based on its footprint extremity to select and rank the collection of events. Several surge footprint types are obtained based on the most impacted coastal stretch from each particular event, and these are linked to the driving storm tracks. Using the collection of the extreme surge events, we assess the spatial distribution and interannual variability of the duration, size, severity, and type. We find that the northeast coastline is most impacted by the longest and largest storm surge events, while the English Channel experiences the shortest and smallest storm surge events. The interannual variability indicates that the winter seasons of 1989-90 and 2013–14 were the most serious in terms of the number of events and their severity, based on the return period along the affected coastlines. The most extreme surge event and the highest number of events occurred in the winter season 1989–90, while the proportion of events with larger severities was higher during the winter season 2013–14. This new spatial analysis approach of surge extremes allows us to distinguish several categories of spatial footprints of events around the UK/Ireland coast and link these to distinct storm tracks. The spatial dependence structures detected can improve multivariate statistical methods which are crucial inputs to coastal flooding assessments.

Carbon Dynamics of a Coastal Wetland Transitioning to Mangrove Forest

AbstractCoastal wetlands play a vital role in the global carbon cycle and are under pressure from multiple anthropogenic influences. Altered hydrology and land use change increase susceptibility of wetlands to sea‐level rise, saltwater intrusion, tidal flood events, and storm surges. Flooding from perigean spring tides and storm surges rapidly inundates coastal wetlands with saline waters, quickly surpassing vegetation tolerances, leading to shifts in soil microbial respiration, peat collapse, and plant mortality, followed by establishment of salt‐tolerant vegetation. The Southeast Saline Everglades (SESE) is facing many of these pressures, making it a model system to examine the impacts of ecosystem state transitions and their carbon dynamics. Saltwater flooding from Hurricane Irma (2017) initiated a transitional state, where less salt‐tolerant vegetation (e.g., Cladium jamaicense) is declining, allowing halophytic species such as Rhizophora mangle to colonize, altering the ecosystem's biogeochemistry. We utilized eddy covariance techniques in the SESE to measure ecosystem fluxes of CO2 and CH4 in an area that is transitioning to an alternative state. The landward expansion of mangroves is increasing leaf area, leading to greater physiological activity and higher biomass. Our site was presented initially as a small C source (47.0 g C m−2) in 2020, and by 2022 was a sink (−84.24 g C m−2), with annual greenhouse carbon balance ranging from −0.04 to 0.18. Net radiative forcing ranged from 2.04 to 2.27 W m−2 d−1. As the mangrove landward margin expands, this may lead the area to become a greater carbon sink and a potential offset to increasing atmospheric CO2 concentrations.

Management implications of habitat selection by whooping cranes (Grus americana) on the Texas coast

AbstractEffective habitat management for rare and endangered species requires a thorough understanding of their specific habitat requirements. Although machine learning models have been increasingly used in the analyses of habitat use by wildlife, the primary focus of these models has been on generating spatial predictions. In this study, we used machine learning models in combination with simulated management actions to guide planning and inform managers. We used data from 61 whooping cranes (Grus americana) tagged with GPS telemetry collars between 2009 and 2018 near Aransas National Wildlife Refuge in coastal Texas. We included variables based on topography, land use classification, vegetation height, plant phenology, drought, storm surge events, and both wild and prescribed fires. We then built models at multiple scales: population level, home range level, and roosting and daytime within home range level. We simulated responses to the two primary management actions used to enhance whooping crane habitat on Aransas National Wildlife Refuge: prescribed fire and removal of woody vegetation. At the population and home range scales, land use classification variables had the highest importance values, whereas the combined elevation and bathymetry layer was the most important predictor at both roosting and daytime within home range scales. Our findings revealed that the effects of fire, although generally modest, varied spatially. Areas dominated by estuarine wetlands exhibited higher predicted use within the first months after a fire, whereas those dominated by palustrine wetlands were more likely to be avoided in the immediate postfire years. Our simulation of vegetation removal identified the areas on Aransas National Wildlife Refuge where whooping cranes were predicted to benefit the most if vegetation were removed. These techniques can be used by other researchers wanting to examine and predict the effects of potential management actions on target species habitat.

Trend of Storm Surge Induced by Typical Landfall Super Typhoons During 1975–2021 in the Eastern China Sea

Trend of Storm Surge Induced by Typical Landfall Super Typhoons During 1975–2021 in the Eastern China Sea

An advanced GIS based approach for the assessment of coastal inundation in the storm surge region (Krishna District) of Andhra Pradesh, INDIA

Climate change leads coastal areas to suffer from rising sea levels, flooding, storm surges and cyclones which can cause irreparable harm to people and their assets. The mapping of these alterations to the coastline can assist in sustainable coastal development and surveillance. The current research emphasized the susceptibility of coastal inundation in the Krishna District of Andhra Pradesh, which is the State’s most vulnerable place to flooding and cyclonic storms. The Indian Meteorological Department website (mausam.imd.gov.in) was accessed to get latitudinal and longitudinal data on cyclonic storms that hit the area recently. The satellite imagery was acquired from Google Earth and USGS (earthexplorer.usgs.gov) to map the coastal settlements within 25 km radius of the coastline and to generate Digital Elevation Model (DEM) to identify the coastal inundation vulnerability up to 5.0 M contour elevation (2m, 3m, 4m and 5m). According to findings, there was a small impact of coastal inundation up to 4 meter sea level rise, so the research primarily concentrated on the assessment of coastal inundation with 5 meter sea level rise. A total of 512 settlements studied for coastal inundation encompassed an area of about 11405.4 hectares. The DEM identified that a large number of settlements in the Krishna District are on the verge of coastal inundation. About 125 settlements (24.5% settlements) with an area of approximately 2099.5 hectares are most vulnerable to coastal submergence, accounting for 18.5% of the studied area. This damage exposure assessment helps local government and policymakers to counteract extreme inundation events.

Coastal vulnerability assessment to multi hazards in the exposed coast of Southeastern Coastal Region of Bangladesh

Bangladesh is a maritime nation situated on the northern side of the Bay of Bengal (BoB), blessed with substantial natural resources and opportunities. However, natural disasters and climate-induced hazards are threatening these opportunities as well as destroying the recourses. Coastal vulnerability assessment aids in the prevention of loss of life and resource devastation since it provides forewarning of impending calamitous consequences and facilitates taking mitigative measures. The study has endeavored to make a composite coastal vulnerability index (CVI) for the exposed coast of the southeastern coastal region of Bangladesh, combining physical and social vulnerability indexes using several physical and social parameters. The CVI was developed by using nine physical parameters, namely (a) Coastal Elevation, (b) Coastal Slope, (c) Annual Average Rainfall, (d) Storm Surge Height, (e) Soil Salinity, (f) Average Wind Speed, (g) Rate of SLR, (h) Soil Permeability, (i) Mean Tidal Range and six social parameters namely (a) Literacy rate, (b) Household Structure, (c) Land Use Land Cover (LULC), (d) Population Density (e) Number of Disabled People and (f) Number of Population (Age: <5 & >65). With the aid of field and secondary data, thematic layers of each physical and social parameter were prepared by defining five distinct vulnerability classes (i.e., very high, high, moderate, low, and very low) and assigning vulnerability ranks. The final CVI was estimated using geospatial techniques such as GIS and remote sensing, coupled with Physical Vulnerability Index (PVI) and Social Vulnerability Index (SVI), and relevant values were categorized into five distinct groups as before. The findings show that 25.9% of the exposed coast in the southeast coastal region, which comprises Banshkhali, Chakaria, Maheshkhali, and Cox's Bazar Sadar is high to very high vulnerable to multiple hazards. Comparatively, 28.29% have been classified as moderately vulnerable, and the remaining 45.8% in the areas of Hathazari, Mirsharai, Sitakunda, Sandwip, and Chittagong City (Kotwali), Kutubdia show that they are very low to low vulnerable to such multi-hazards. Based on this vulnerability assessment result, sustainable coastal management policy-making and development planning can be taken to mitigate the risk and loss of the southeastern coastal region due to climatic hazards and natural disasters such as floods and storm surges. The outcome information will also help to make an Integrated Coastal Zone Management (ICZM) plan for the proper and sustainable utilization of the coastal resources. Additionally, it will assist as ground information to establish a framework for prioritizing community resilience-building initiatives or developing appropriate adaptation strategies to lower catastrophe risk.

Influence of bed roughness parameter in storm surge modeling along the east coast of India

Influence of bed roughness parameter in storm surge modeling along the east coast of India