Coastal Hazard Assessment Research Articles

Remote Measurement of Tide and Surge Using a Deep Learning System with Surveillance Camera Images

The latest progress in deep learning approaches has garnered significant attention across a variety of research fields. These techniques have revolutionized the way marine parameters are measured, enabling automated and remote data collection. This work centers on employing a deep learning model for the automated evaluation of tide and surge, aiming to deliver accurate results through the analysis of surveillance camera images. A mode of deep learning based on the Inception v3 structure was applied to predict tide and storm surges from surveillance cameras located in two different coastal areas of Italy. This approach is particularly advantageous in situations where traditional tide sensors are inaccessible or distant from the measurement point, especially during extreme events that require accurate surge measurements. The conducted experiments illustrate that the algorithm efficiently measures tide and surge remotely, achieving an accuracy surpassing 90% and maintaining a loss value below 1, evaluated through Categorical Cross-Entropy Loss functions. The findings highlight its potential to bridge the gap in data collection in challenging coastal environments, providing valuable insights for coastal management and hazard assessments. This research contributes to the emerging field of remote sensing and machine learning applications in environmental monitoring, paving the way for enhanced understanding and decision-making in coastal regions.

Unstructured swan modelling of free infragravity waves over the Southern North Sea

Infragravity (IG) waves are relatively long waves with typical periods of several tens of seconds to several minutes. The energy at the IG band plays an important role in nearshore areas. For example, IG waves can significantly contribute to dune erosion and sediment transport (e.g., Roelvink et al., 2009), and may excite harbor oscillations (e.g., Bowers, 1977). Furthermore, IG waves may result in destructive inundation events (e.g., Roeber and Bricker, 2015). These documentations of IG waves' impacts emphasise the necessity to account for IG contributions as part of coastal hazard assessments, especially under storm conditions.

Coastal Hazard Assessment from Multidecadal Profile Response Incorporating Climate Patterns and Sea-Level Rise

Coastal Hazard Assessment from Multidecadal Profile Response Incorporating Climate Patterns and Sea-Level Rise

A National‐Scale Coastal Flood Hazard Assessment for the Atoll Nation of Tuvalu

AbstractAtoll nations such as Tuvalu are considered to be amongst those most vulnerable to the effects of climate change. Here we present a national‐scale coastal flood hazard assessment for Tuvalu based on high‐resolution Light Detection and Ranging (LiDAR) topography and bathymetry. We follow a fully probabilistic approach, considering sea level anomalies, tides, and extreme wave conditions from a mixed climate (i.e., from distant extra‐tropical storms and local tropical cyclones). Nearshore processes such as wave setup and runup are also accounted for. Hazard maps were calculated for the present sea level, as well as for sea level rise projections corresponding to different shared socioeconomic pathways (SSP2 4.5 and SSP5 8.5) and time horizons (2060 and 2100). With a mean elevation of 1.55 m above mean sea level (1.37 m above mean high water spring) >25% of land area is inundated once every 5 years and >50% of land area floods once every 100 years nationally. Results indicate that present day 1‐in‐50 years floods (>45% of land area flooded) will occur more than once every 5 years by 2060 (annual exceedance probability >20%), even under the moderate SSP2 4.5 sea level rise projections. Results of this study highlight the pressing need for ambitious and large‐scale adaptation solutions which are commensurate with projected sea level rise and marine hazard impacts. The methodologies presented in this paper can easily be applied to other low‐lying islands in the tropical Pacific, where mixed climates (i.e., regular and TC conditions) and non‐linear nearshore processes dominate extreme water levels and flooding.

Combined surge-meteotsunami dynamics: A numerical model for hurricane Leslie on the coast of Portugal

In recent years, Portugal's coastal regions have experienced an increase in the frequency and intensity of severe weather events, including tropical cyclones and extratropical storms. This paper presents an analysis of Hurricane Leslie(2018)'s impact on Portugal, with a specific focus on the complex and often underestimated meteotsunami phenomena accompanying the storm system. Our analysis examines data collected from multiple sources, and employs advanced numerical simulations, integrated within the GeoClaw framework. These simulations encompass both storm surge and meteotsunami effects. One of the findings is the significant role played by meteotsunamis in amplifying coastal sea levels during extreme weather events. The observed sea-level fluctuations closely align with the combined surge-meteotsunami simulations, emphasizing the importance of considering these high-frequency phenomena in coastal hazard assessments.

Community-Centric Approaches to Coastal Hazard Assessment and Management in Southside Norfolk, Virginia, USA

Urban communities in environmentally sensitive areas face escalating challenges due to climate change and inadequate infrastructural support, particularly in underserved regions like southside Norfolk, Virginia. This area, characterized by its vulnerability to flooding and a predominantly low-income population, lacks equitable inclusion in broader urban flood protection plans. This research focuses on the development of community-centered resilience strategies through active engagement and collaboration with local residents. The methodology centered around building trust and understanding within the community through a series of interactions and events. This approach facilitated a two-way exchange of information, enabling the research team to gather crucial insights on community-valued assets, prevalent flooding issues, and preferred flood mitigation solutions. The engagement revealed a significant increase in community knowledge regarding climate change, sea level rise, and stormwater management. Residents expressed a strong preference for green infrastructure solutions, including rain gardens, permeable pavements, and living shorelines, alongside concerns about pollution and the need for infrastructure redesign. The outcomes of this community engagement have initiated plans to develop tailored, nature-based flooding solutions. These results are set to inform future urban planning and policy, offering insights to the City of Norfolk and the United States Army Corps of Engineers for potential redesigns of flood intervention strategies that are more inclusive and effective. A template for participatory research to inform coastal hazard management includes cross-sector collaboration, a long-term engagement commitment, and education and surveying opportunities to align solutions to lived, local experiences. This template allows for community trust building, which is especially important in environmental justice communities. The study highlights the importance of community involvement in urban resilience planning, demonstrating that local engagement is essential in shaping community-centric solutions and equitable environmental policies.

Machine learning motivated data imputation of storm data used in coastal hazard assessments

In the Coastal Hazards System's (CHS) Probabilistic Coastal Hazard Analysis (PCHA) framework developed by the United States Army Corps of Engineers (USACE), historical records of tropical cyclone parameters have been used as data sources for statistical analysis, including fitting marginal distributions and measuring correlations between storm parameters. One limitation of the available historical databases is that observations of central pressure and radius of maximum winds are not available for a large number of storms. This may adversely affect the results of statistical analyses used to develop hazard curves. This study uses machine learning techniques to develop a data imputation method to “fill in” missing storm parameter records in historical datasets used for Joint Probability Method (JPM)-based coastal hazard analysis such as the USACE's CHS-PCHA. Specifically, Gaussian process regression (GPR) and artificial neural network (ANN) models are investigated as candidate machine learning-derived data imputation models, and the performance of different model parameterizations is assessed. Candidate imputation models are compared against existing statistical relationships. The effect of the data imputation process on statistical analyses (marginal distributions and correlation measures) is also evaluated for a series of example coastal reference locations.

Dynamic Modeling of Coastal Compound Flooding Hazards Due to Tides, Extratropical Storms, Waves, and Sea-Level Rise: A Case Study in the Salish Sea, Washington (USA)

The Puget Sound Coastal Storm Modeling System (PS-CoSMoS) is a tool designed to dynamically downscale future climate scenarios (i.e., projected changes in wind and pressure fields and temperature) to compute regional water levels, waves, and compound flooding over large geographic areas (100 s of kilometers) at high spatial resolutions (1 m) pertinent to coastal hazard assessments and planning. This research focuses on advancing robust and computationally efficient approaches to resolving the coastal compound flooding components for complex, estuary environments and their application to the Puget Sound region of Washington State (USA) and the greater Salish Sea. The modeling system provides coastal planners with projections of storm hazards and flood exposure for recurring flood events, spanning the annual to 1-percent annual chance of flooding, necessary to manage public safety and the prioritization and cost-efficient protection of critical infrastructure and valued ecosystems. The tool is applied and validated for Whatcom County, Washington, and includes a cross-shore profile model (XBeach) and overland flooding model (SFINCS) and is nested in a regional tide–surge model and wave model. Despite uncertainties in boundary conditions, hindcast simulations performed with the coupled model system accurately identified areas that were flooded during a recent storm in 2018. Flood hazards and risks are expected to increase exponentially as the sea level rises in the study area of 210 km of shoreline. With 1 m of sea-level rise, annual flood extents are projected to increase from 13 to 33 km2 (5 and 13% of low-lying Whatcom County) and flood risk (defined in USD) is projected to increase fifteenfold (from 14 to USD 206 million). PS-CoSMoS, like its prior iteration in California (CoSMoS), provides valuable coastal hazard projections to help communities plan for the impacts of sea-level rise and storms.

High-resolution coastal flooding hazard assessment with pumping stations under climate change

High-resolution coastal flooding hazard assessment with pumping stations under climate change

Evaluation of Parametric Tropical Cyclone Surface Winds over the Eastern Australian Region

Abstract Hazard studies based on thousands of synthetic tropical cyclone (TC) events require a validated model representation of the surface wind field. Here, we assess three different TC parametric vortex models with input from four along-track parameter studies of the TC size and shape, based on statistical formulation of the relationships to observed TC intensity, geographic location, and forward transition speed. The 12 model combinations are compared to in situ 10-min observed surface mean wind speeds for 10 TCs that made landfall over Queensland, Australia, which occurred over the period 2006–17. Empirical wind reduction factors to reduce gradient winds to the surface are recalculated for the more recent TCs at both offshore (ocean, small islands, reefs, and moorings) and onshore (land) locations. To improve the wind comparisons over ocean and land, a secondary reduction factor was developed based on an inland decay function. Pearson correlations for the unadjusted modeled peak wind speed from 118 instances of a TC passing a weather station sit between a range of 0.57 and 0.65 for the 12 model combinations. Using the secondary reduction factor based on the inland decay function increases the range of correlation to 0.74–0.81. Based on the assessment of the instances of peak surface wind speed correlations, bias, and root-mean-square error, along with the correlation 48 h around the peak, the top-ranked performing model combination for the region was an along-track parameter study with a double-vortex model, both previously tested for the South Pacific basin. Significance Statement When assessing tropical cyclone hazards, users are presented with several simplified parametric models to describe the surface wind field of tropical cyclones. These parametric models are used routinely for risk assessment of cyclonic winds, as well as for input to surge and wave models used in coastal hazard assessments. Differences between the models include the formulation of the parametric cyclone model, the way winds above the boundary layer are specified at the surface and along-track parameters that describe the cyclones’ size and shape. Of the 12 model combinations investigated in this study, the top-ranked performing model combination for the region was an along-track parameter equation with a double-vortex model, which were both tested previously for the South Pacific basin. Analysis is performed to show unadjusted modeled winds overestimate observed 10-min surface winds over the ocean by around 13% (median) and over land by around 73.9% (median), which is resolved in this study with a secondary empirical wind reduction factor. These findings will support future modeling of tropical cyclone winds for multiple applications, including regional risk assessment and coastal hazard studies.

Shoreline Change Analysis with Deep Learning Semantic Segmentation Using Remote Sensing and GIS Data

Shoreline management is essential for navigation, coastal resource management, and coastal planning and development. Shoreline change detection is vital for shoreline monitoring; however, traditional methods used for such detection are laborious and have limited accuracy. An approach that integrates remote sensing imagery and geographic information systems (GISs) is proposed herein to simultaneously identify shoreline changes and perform grid-level visualization for updating shoreline data. The integrated approach uses deep learning-based segmentation networks and water indexes to accurately classify land and sea in remote sensing images. Transfer learning was used to address the issue of insufficient data, wherein weights trained on a large open dataset were applied to the target area. The segmentation results were compared with existing shoreline GIS data to identify the areas experiencing shoreline changes. Grid-level visualization enhanced the identification of regions requiring flexible data updates and investigation efficiency by focusing on specific areas. The proposed approach accurately detected shoreline changes, albeit with some errors of commission, predominantly in regions featuring intricate shorelines and small clusters of islands. The proposed approach offers efficient solutions for shoreline change detection, with potential applications in coastal management, environmental science, urban planning, and coastal hazard assessment.

Coastal Inundation Hazard Assessment in Australian Tropical Cyclone Prone Regions

One of the hazards associated with tropical cyclones (TCs) is a storm surge, which leads to coastal inundation and often results in loss of life and damage to infrastructure. In this study, we used GIS-based bathtub models and tide-gauge-derived water levels to assess coastal inundation scenarios for the landfall region of TC Debbie. The three scenarios modelled what could have happened if the TC’s maximum storm surge had coincided with the maximum storm tide for that day, month, or TC season, where the water levels were determined through analysis of tide gauge data, using a new method called the variable enhanced Bathtub Model. Additionally, this study analysed the impact of excluding the correction of water levels with the Australian Height Datum. Our study found that between the least and most severe scenarios, with the input water-level difference for the model along the coastline being 0.43 m, the observed inundation depth of the analysed populated region increased from 0.25 m to 1 m. Ultimately, it was found that in the worst-case scenario, the study region could have experienced coastal inundation 0.63 m higher than it did, inundating 72.53 km2 of the coast. The results of this study support the consensus that coastal inundation is highly dependent on the characteristics of the terrain, and that coastal inundation modelling, such as that completed in this study, needs to be performed to better inform decision makers and communities of the potential impacts of TC-induced storm surges.

A framework for coastal flood hazard assessment under sea level rise: Application to the Persian Gulf

Coastal areas are of paramount importance due to their pivotal role in facilitating a wide range of socio-economic activities and providing vital environmental services. These areas, as the meeting points of land and sea, face significant risks of flooding due to the ongoing rise in sea levels caused by climate change. Additionally, they are susceptible to extreme events like king tides and large waves in the future. This paper introduces a framework for estimating the extreme total water level (TWL) by considering the effects of regional sea level rise (RSLR) resulting from a warming climate under RCP 8.5. It also incorporates the contributions of high tides, 100-year storm surge, and 100-year wave setup and run-up. The proposed framework is utilized to evaluate the occurrence of extreme coastal flooding along the Persian Gulf coast of Iran, an area that is home to significant industries in the country. The results offer an estimated increase of RSLR by 0.23 m from 2020 to 2050 considering an ensemble of climate model projections. Extreme wave setup values are estimated to range between 0.19 and 0.66 m, while storm surge is projected to vary from 0.4 to 1.44 m across the studied coastline. These together yield in a projected extreme TWL along the coastline within the range of 3.18 and 3.90 m above the current sea level. This significant increase in sea level could lead to the inundation of approximately 513 km2 of low-lying coastal land, which accounts for about 16% of the studied domain and could pose serious flooding threat to the people and their assets in this region. Finally, relative ranking of flooded zones (i.e., 6 zones) helps determine the areas with higher chance of flood exposure, at which investment in flood mitigation measures should be prioritized.

Reciprocity and sensitivity kernels for sea level fingerprints

SUMMARYReciprocity theorems are established for the elastic sea level fingerprint problem including rotational feedbacks. In their simplest form, these results show that the sea level change at a location x due to melting a unit point mass of ice at x′ is equal to the sea level change at x′ due to melting a unit point mass of ice at x. This identity holds irrespective of the shoreline geometry or of lateral variations in elastic Earth structure. Using the reciprocity theorems, sensitivity kernels for sea level and related observables with respect to the ice load can be readily derived. It is notable that calculation of the sensitivity kernels is possible using standard fingerprint codes, though for some types of observable a slight generalization to the fingerprint problem must be considered. These results are of use within coastal hazard assessment and have a range of applications within studies of modern-day sea level change. To illustrate the latter point, we use sensitivity kernels to investigate two widely used methods for estimating, respectively, ice sheet mass loss from satellite gravity, and rates of global mean sea level rise from satellite altimetry. Though our analysis is idealized in some respects, we identify systematic errors of order 5 per cent due to the use of simplified sea level physics. Crucially, calculation of the relevant sensitivity kernels provides not only a means for understanding sources of bias in existing methods, but will aid in the design of new and improved data-assimilation techniques.

On a ~210 t Caribbean coastal boulder: The huracanolito seaward of the ruins of the Bucanero resort, Juragua, Oriente, Cuba

AbstractCoastal boulder deposits (CBDs), named huracanolitos in Cuba, found along rocky shores, result from storms, tropical cyclones or tsunamis. Despite being important indicators for coastal hazard assessment, determining the mode of emplacement of CBDs (storm/hurricane or tsunami) is not easy. We present, for the first time in English, data about CBDs along the shores of the Cuban Archipelago. More specifically, we focused on a CBD, that is, to our knowledge, the largest one ever described on Cuba Island. Located on a low‐lying coral reef terrace on the SE shore of the island, the reefal limestone CBD is emplaced seaward of the ruins of the Bucanero resort. The resort was built in 1989, endured hurricanes Ivan (2004) and Dennis (2005) and, in October 2012, was destroyed by Hurricane Sandy. As observed on Corona and Landsat satellite images since 1962, the CBD was not moved, neither by hurricane Flora (1963) nor Sandy (2012), both associated with important storm surges and powerful swells. We determined the CBD volume with open‐source structure from motion photogrammetry as 82.6 m3. Then, we estimated from a sample its density as 2550 kg/m3. Finally we calculated its weight as 210.6 tons. We calculated the minimum flow velocity responsible for the emplacement of the CBD 33 ± 2 m inland—6.83 ± 0.54 m/s and 7.26 ± 0.51 m/s. Such flow velocities are compatible with those of both tsunamis and hurricanes. Because of the greater frequency of hurricanes than tsunamis in the area, we propose that a tropical cyclone generated the extreme surge and wave that emplaced the Bucanero CBD. Such CBDs demonstrate that on Cuba's south coast, we can expect marine flooding exceeding the flooding during the major hurricanes of recent decades.

Arctic coastal hazard assessment considering permafrost thaw subsidence, coastal erosion, and flooding

The thawing of permafrost in the Arctic has led to an increase in coastal land loss, flooding, and ground subsidence, seriously threatening civil infrastructure and coastal communities. However, a lack of tools for synthetic hazard assessment of the Arctic coast has hindered effective response measures. We developed a holistic framework, the Arctic Coastal Hazard Index (ACHI), to assess the vulnerability of Arctic coasts to permafrost thawing, coastal erosion, and coastal flooding. We quantified the coastal permafrost thaw potential (PTP) through regional assessment of thaw subsidence using ground settlement index. The calculations of the ground settlement index involve utilizing projections of permafrost conditions, including future regional mean annual ground temperature, active layer thickness, and talik thickness. The predicted thaw subsidence was validated through a comparison with observed long-term subsidence data. The ACHI incorporates the PTP into seven physical and ecological variables for coastal hazard assessment: shoreline type, habitat, relief, wind exposure, wave exposure, surge potential, and sea-level rise. The coastal hazard assessment was conducted for each 1 km2 coastline of North Slope Borough, Alaska in the 2060s under the Representative Concentration Pathway 4.5 and 8.5 forcing scenarios. The areas that are prone to coastal hazards were identified by mapping the distribution pattern of the ACHI. The calculated coastal hazards potential was subjected to validation by comparing it with the observed and historical long-term coastal erosion mean rates. This framework for Arctic coastal assessment may assist policy and decision-making for adaptation, mitigation strategies, and civil infrastructure planning.

MONTE CARLO SIMULATION OF BARRIER-ISLAND SYSTEMS AND TSUNAMI HAZARDS

Robust characterization of the future tsunami hazard is critically important for resilient planning and engineering in coastal communities prone to tsunami inundation. The hazard from earthquake-generated tsunami waves is not only determined by the earthquake's characteristics and distance to the earthquake area, but also by the geomorphology of the nearshore and onshore areas, which can change over time. In coastal hazard assessments, a changing coastal environment is commonly taken into account by increasing the sea-level to projected values (static). However, sea-level changes and other climate-change impacts influence the entire coastal system causing morphological change (dynamic). Here, we present the modeling framework and results initially published in Weiss et al. (2022), which employs within a Monte Carlo framework the barrier island-marsh, lagoon- marsh evolution model of Lorenzo-Trueba and Mariotti (2017) and the tsunami model Geoclaw (e.g., LeVeque et al. 2011). We compare the runup of the same suite of earthquake-generated tsunamis to a barrier system for statically adjusted and dynamically adjusted sea level and bathymetry over the period from 2000 to 2100. We employ Representative Concentration Pathways 2.6 and 8.5 without and with treatment of Antarctic ice-sheet processes (e.g., Kopp et al. 2017) as different sea-level projections.

MODELLED AND OBSERVED IMPACT OF THE APRIL 2021 SOUTHERN OCEAN STORM

Coastal hazards exacerbated by sea level rise present a growing protection gap to property owners and the wider community. ‘Actions of the Sea’ have been a traditional exclusion from insurance policies given their inevitable nature and this present a reputational risk to the industry. Insurance issues regarding coastal inundation and erosion are explored using a case study approach, investigating extreme events impacting Sydney, Australia. A novel multidisciplinary approach combining expertise of coastal hazard assessment and engineers, insurance professionals and urban planning was applied to unravel the deep complexities of the problem. National scale recommendations are formed from the technical findings. The case study focuses on the June 2016 storm event impacting Collaroy/Narrabeen.

IMPACT OF INCLUDING OVERTOPPING IN HYDRODYNAMIC MODELLING FOR COASTAL INUNDATION IN PORT PHILLIP BAY

During 2019-2021 CSIRO led the Port Phillip Bay Coastal Hazard Assessment (PPBCHA) project, concerning the bay located in Victoria, Australia. This study looked into the three main hazards of inundation, erosion and groundwater. As part of the inundation hazard component, coastal inundation involving wave setup, seawall overtopping, drainage and urban flooding was considered. Computational modelling was the tool used to study these key elements, necessitating the development of a coupled hydrodynamic and overtopping model. This paper details the assessment of the overall flooding sensitivity to the inclusion of the overtopping modelling, surge (storm tide), rainfall, and drainage.

The Global Drivers of Chronic Coastal Flood Hazards Under Sea‐Level Rise

AbstractWe present the first global estimates of annual average exceedances of contemporary minor, moderate, and major flood levels under sea‐level rise (SLR). Applying established methods, we show that minor flooding will occur most days worldwide under 0.7 m global SLR. Moderate flooding occurs at the same frequency under 1.0 m SLR. Local and regional differences in flood threshold elevations, tidal ranges, and non‐tidal variability lead to differences in the SLR required for this chronic flooding to emerge. Lower flood thresholds, smaller tidal ranges, and larger extreme skew surges mean chronic flooding can emerge with less SLR. We discuss several implications of these findings for coastal flood hazard assessments. First, tide‐driven water level variability dominates weather‐driven water level variability when determining locations' propensities for frequent and chronic flooding under SLR. Second, centimeter‐accurate flood threshold information is necessary to accurately estimate present and future flood hazards. Third, locations with the most frequent floods at present may not be those that have the most frequent floods under SLR. We develop the Rapid Assessment Framework for Frequent Flood Transitions under SLR (RAFFFTS) to apply these findings to locations not previously considered in global coastal flood hazard studies. RAFFFTS can robustly identify potential future tidal flooding hotspots using only 1‐year observational records. We anticipate RAFFFTS will be a valuable tool for identifying locations at risk of chronic flooding under SLR, complementing existing tools for identifying changes in less frequent episodic floods.