Coastal Disaster Assessment and Response

Coastal communities have become increasingly susceptible to natural hazards over the past few decades, largely due to the effects of climate change [1,2]. The rising frequency and intensity of storms place immense pressure on coastal populations, particularly in low-lying areas, and in regions where economic and logistical challenges often hinder evacuation and recovery efforts [3,4]. Similarly, tsunamis pose a severe threat to coastal communities, especially those near tectonic subduction zones [5]. Communities lacking adequate tsunami early warning systems or evacuation plans face heightened vulnerability, underscoring the urgent need for enhanced disaster preparedness and adaptive resilience strategies.

Traditional Disaster Risk Reduction (DRR) efforts have primarily focused on response and recovery. However, integrating DRR with climate adaptation strategies offers a more proactive approach, enabling local communities to effectively respond to coastal hazards [6]. A key element of building resilience is the implementation of nature-based solutions, which act as natural barriers to mitigate these risks [7,8,9]. Nature-based approaches can not only protect coastal populations but also enhance biodiversity and ecosystems. Likewise, advances in Geographic Information Systems (GISs) and climate modeling could improve hazard prediction and risk mapping, facilitating improved planning and response measures [10,11,12,13]. At the same time, empowering local communities through participatory decision-making and knowledge sharing could ensure that adaptation strategies align with their specific needs. Considering these factors, this Special Issue brings together innovative research on risk assessment and enhancing community resilience in response to coastal hazards.

The article by Yoo and Kwon (contribution 1) explores the liquefaction and reliquefaction behavior of coastal embankments subjected to repeated seismic loading. Through a shaking table experiment, they investigate how different ground conditions influence soil response under cyclic loading with a particular focus on the effects of groundwater fluctuations. Their findings indicate that when an upper non-liquefiable layer is present, liquefaction may not occur initially but can develop after multiple seismic excitations. As repeated earthquakes cause the groundwater level to rise, liquefaction is eventually triggered, even in cases where the main seismic event does not cause immediate failure. Significantly, the study highlights that aftershocks can contribute to liquefaction due to the cumulative effect of groundwater migration. These insights challenge current seismic design codes, which typically assess liquefaction risk only for soil layers below the groundwater table. To enhance the earthquake resilience of coastal infrastructure, the authors stress the importance of incorporating aftershock-induced liquefaction risk and groundwater level variations into hazard assessment frameworks.

Recognizing the growing risks associated with storm-induced coastal processes, Chalmoukis (contribution 2) assesses vulnerability levels through morphodynamic simulations using the Storm-induced BEAch CHange (SBEACH) model. These simulations are combined with inundation estimates derived from two empirical equations for comparative analysis. By correlating both hazards with return period-based assessments, high-risk coastal areas are identified, offering crucial insights for hazard mitigation planning. A key takeaway from Chalmoukis’s research is the importance of accurate hazard modeling to ensure reliable vulnerability mapping. The study highlights the limitations of existing predictive models, many of which assume smooth coastal profiles and uniform seabed conditions—assumptions that may not hold for irregular terrains with submerged rocks and varying sediment grain sizes. Chalmoukis provides an initial large-scale vulnerability assessment, which serves as a valuable guide for optimizing disaster preparedness strategies for coastal regions.

In their study, Nezhad et al. (contribution 3) explore the potential of ENNs to enhance the accuracy, reliability, and robustness of storm surge predictions compared to single-model approaches, highlighting the increasing importance of accurate flood prediction in coastal disaster management. A key focus of Nezhad et al.’s study is the techniques used to aggregate predictions from multiple neural network models, demonstrating how the integration of diverse models can lead to more reliable flood forecasts. The study reveals that there is no universal approach to constructing an optimal ENN model for storm surge forecasting. Instead, it is necessary to assess different ensemble configurations to strike a balance between bias and variance, ensuring a trade-off between accuracy, diversity, stability, generalization, and computational efficiency. Nezhad et al.’s findings are particularly relevant for coastal researchers, engineers, and planners, emphasizing the growing role of ENNs in storm surge forecasting, supporting better preparedness and response strategies for coastal hazards. The authors were awarded the Editor’s Choice Article Award in recognition of their work.

Li et al. (contribution 4) assess the applicability of the Global Synthetic Tropical Cyclone Hazard (GSTCH) dataset for tropical cyclone hazards. To evaluate its reliability, the authors compare it with the Tropical Cyclone Best Track (TCBT) dataset, an authoritative dataset developed by the China Meteorological Administration. The findings indicate that key cyclone characteristics, such as landfall wind speed, central pressure at landfall, and annual cyclone frequency, show no statistically significant differences between the two datasets at a 95% confidence level. Additionally, the cumulative distributions of the central maximum wind speed and central minimum pressure along cyclone tracks pass the Kolmogorov–Smirnov test, confirming that the GSTCH dataset aligns with the TCBT dataset at the provincial and regional scales. They conclude that the GSTCH dataset is a reliable and applicable data source for tropical cyclone hazard assessments, demonstrating strong agreement with the widely accepted TCBT dataset. These findings reinforce the credibility of the GSTCH dataset as a valuable tool for long-term cyclone hazard studies and risk assessments.

In their study, Hwang et al. (contribution 5) examine the morphological changes in coastal areas following the Super Typhoon Hinnamnor (2022), emphasizing the need for accurate observation and modeling to support coastal management. They employ the XBeach model to simulate pre- and post-storm subaerial data. To enhance model accuracy, Hwang et al. place particular emphasis on waveform parameters, including wave skewness and asymmetry, assessing whether these factors should be treated independently or integrated to improve the representation of sediment deposition from overwash events. They evaluate the model’s performance and sensitivity by analyzing volume changes, revealing that waveform parameters significantly impact deposition patterns. Their findings provide valuable insights into optimizing parameter selection and calibration for models simulating coastal sediment transport, improving predictive accuracy in coastal morphological modeling.

Velioglu Sogut et al. (contribution 6) investigate the performance of two numerical approaches in estimating non-equilibrium foundation scour patterns around a non-slender square structure subjected to a transient wave. They compare numerical results with experimental data to evaluate the accuracy and effectiveness of these approaches. The first numerical approach models sediment particles as a separate continuum phase, directly solving continuity and momentum equations for both sediment and fluid phases. The second approach predicts sediment transport using the quadratic law of bottom shear stress, achieving accurate bed evolution estimates through careful calibration and validation. Their findings underscore notable differences in the predictive capabilities of both methods, particularly in modeling non-equilibrium scour evolution at low Keulegan–Carpenter numbers. Their work provides valuable insights into the strengths and limitations of these numerical approaches, contributing to advancing scour prediction techniques in coastal engineering applications.

In their study, Ma et al. (contribution 7) examine the impact of vegetation on wave attenuation and dune erosion, using the XBeach surfbeat model (XBSB) to simulate conditions at Mexico Beach during Hurricane Michael (2018). They investigate how different vegetation drag coefficients influence wave energy reduction and dune stability. Their findings highlight the significant role of dune vegetation in wave attenuation and erosion control. Ma et al. emphasize that as vegetation drag coefficients increase, wave energy dissipation improves, resulting in less dune erosion. Additionally, higher vegetation density enhances wave height reduction and flow velocity damping within vegetated areas. However, the findings suggest that beyond a certain density, the rate of improvement in wave attenuation diminishes, providing negligible additional benefits. Ma et al.’s results emphasize the vital role of coastal vegetation in enhancing resilience against storm-induced impacts, offering valuable insights for coastal management strategies and disaster mitigation planning.

In their work, Corkran et al. (contribution 8) analyze the spatiotemporal trends of tropical cyclones (TCs) affecting the state of Georgia, recognizing that research on Georgia-specific TCs remains limited due to the state’s small coastline and the infrequency of direct landfalls. Using data from the North Atlantic Basin hurricane database, they quantify both direct and indirect TC landfalls in Georgia from 1851 to 2021. Applying a multi-method approach by combining statistical analysis and mapping, the authors examine 113 tropical cyclones that affected Georgia, identifying September as the month with the highest percentage of TC-induced rainfall, followed by October and August, which aligns with peak TC activity. Their findings highlight the heightened risk posed by TCs during the peak season, emphasizing the need for increased preparedness, strategic resource allocation, and disaster planning to protect Georgia’s communities, historical landmarks, and natural environments from the long-term impacts of TC activity.

In their research, Santos and Mileu (contribution 9) evaluate the tsunami inundation risk in Caxias, Portugal. They generate a Digital Elevation Model (DEM) using newly obtained LiDAR data and integrate it into the TUNAMI-N model to improve the precision of inundation predictions. Additionally, the authors highlight the coastal characteristics and existing protective structures in the region, which are identified through a field survey conducted at various locations. The findings reveal that low-lying areas in the study region would be flooded if a tsunami comparable to the 1755 Lisbon event occurred. To mitigate these risks, the authors recommend constructing seawalls and installing a pedestrian bridge over the Barcarena Stream, which would act as a barrier against incoming tsunami waves. This study underscores the importance of integrating these coastal protection measures into long-term resilience planning to minimize the impacts of tsunamis and winter storm surges, not only in Caxias but also in other vulnerable coastal regions in Portugal.

Hu et al. (contribution 10) examine the 2018 Sulawesi tsunami, utilizing simple and accessible remote sensing techniques to assess the extent of destruction and indirectly evaluate the region’s vulnerability to such disasters. The authors employ Sentinel-2 and Maxar WorldView-3 satellite imagery to analyze the affected areas in Palu, Indonesia, by quantifying changes in vegetation, soil moisture, and water bodies, effectively mapping the tsunami’s impact on land cover. The resulting inundation map reveals that the most heavily affected zones are concentrated in urban centers, low-lying areas, and coastal regions. Unlike high-resolution remote sensing methods that depend on specialized or proprietary tools, Hu et al. emphasize a more accessible and cost-effective approach that is particularly beneficial for resource-limited regions and rapid disaster response efforts. Additionally, the research tackles the challenge of distinguishing tsunami-related damage from other geological phenomena such as liquefaction, utilizing index-based thresholds to enhance classification accuracy. The proposed framework is highly adaptable and can be applied to other vulnerable coastal areas, offering a practical, efficient, and low-cost solution for post-tsunami damage assessment.

The concluding study in this Special Issue, conducted by Mayorga et al. (contribution 11), analyzes the structural response of single-story timber houses to the 2010 Chile tsunami in San Juan Bautista, an island town in the Pacific Ocean. The ASCE 7–22 energy grade line analysis (EGLA) is used to calculate flow depths and velocities, incorporating topographic data and recorded runup measurements. The structural evaluation follows Load and Resistance Factor Design (LRFD) principles, accounting for both dead and live loads. The findings reveal that houses near the shoreline undergo significant displacement and collapse, caused by hydrodynamic forces, drag, and buoyancy effects, which weaken foundation anchorage. In contrast, structures further inland experience lower flow velocities, leading to reduced displacement, lower structural demand, and an increased tendency to float. To verify the approach, Mayorga et al. perform a nonlinear analysis on structures exposed to tsunami forces at varying distances from the coast. Although lightweight timber houses demonstrate strong seismic performance, the authors find them unsuitable for tsunami-prone areas due to their vulnerability to hydrodynamic loads. The research emphasizes the importance of using heavier, more rigid materials in flood-prone areas and relocating lightweight structures to safer zones to improve tsunami resilience in coastal communities.