Search Results (60)

Coastal regions are increasingly threatened by compound flooding due to the increasing intensities of storm surges and rainfall under climate change. However, relevant research has been limited because significant amounts of data, scenarios, and computations are often required to evaluate long-term variations in compound flood risk. In this study, a framework was proposed through efficient hydraulic simulations and a consequence-based statistical method using data projected under different general circulation models (GCMs). The analysis focuses on analyzing the interdecadal trends of compound flood risk for a coastal area in southwestern Taiwan across a baseline period and four future periods in the short-term (2021–2040), mid-term (2041–2060), mid-to-long-term (2061–2080), and long-term (2081–2100). Although discrepancies exist in the short term, the results show that the values of the annual maximum flood area exhibit an increasing pattern in the future for all GCMs by increasing about 27.8% on average at the end of the 21st century. This means that, under the same flood areas given in the baseline period, the return periods will decrease, and flood events will occur more frequently in the future. This framework can be extended to other regions to assess the impacts of compound flooding with different geographical and meteorological conditions. Full article

Effective flood management requires a comprehensive understanding of interactions between multiple flooding sources. This study investigates compound flooding in the Brisbane River Estuary (BRE), Australia, using the MIKE 21 hydrodynamic model to assess the combined effects of tidal and riverine processes on flood extent and water levels. Unlike conventional studies that evaluate these factors separately, this research quantifies the impact of boundary condition variations at the Moreton Bay entrance on flood modelling accuracy. The model was calibrated by adjusting Manning’s n, achieving a Nash–Sutcliffe efficiency (E_ns) ranging from 0.84 to 0.95. Validation results show a 90% agreement between the simulated and observed 2011 flood extent. The findings highlight the critical role of tidal boundary conditions, as their exclusion led to a 0.62 m and 0.12 m reduction in flood levels at Jindalee and Brisbane City gauges, respectively. This study provides valuable insights for improving flood risk assessment, model accuracy, and decision-making in estuarine flood management. Full article

Storm surge forecasting presents a significant challenge for coastal resilience, particularly in typhoon-prone regions such as southeastern China, where compound flooding events lead to substantial socioeconomic losses. Although artificial intelligence (AI) models have shown strong potential in storm surge prediction, their inherent “black-box” nature limits both their interpretability and operational trust. In this study, we integrate a Vision Transformer (ViT) model with an explainable AI (XAI) method—specifically, Shapley value analysis (SHAP)—to develop an interpretable, high-performance storm surge forecasting framework. The baseline ViT model demonstrates excellent predictive skill, achieving spatiotemporal correlation coefficients exceeding 0.90 over a 12 h lead time. However, it exhibits systematic underestimations in topographically complex regions, such as semi-enclosed bays (e.g., up to 0.06 m). SHAP analysis reveals that the model primarily relies on the autocorrelation of historical surge levels rather than external wind forcing—contrary to the conventional physical understanding of storm surge dynamics. Guided by these insights, we introduce the surge time difference (ΔZ/Δt) as an explicit input feature to enhance the model’s physical representation. This modification yields substantial improvements: during the critical first hour of forecasting—a key window for disaster mitigation—the RMSE is reduced from 0.01 m to 0.005 m, while the correlation coefficient increases from 0.92 to 0.98. This study bridges the gap between data-driven forecasting and physical interpretability, offering a transparent and trustworthy framework for next-generation intelligent storm surge prediction. Full article

Extreme flood occurrences are becoming increasingly common due to global climate change, with coastal cities being more vulnerable to compound flood disasters. In addition to excessive precipitation and upstream river discharge, storm surge can complicate the flood disaster process and increase the hazard of urban flooding. This study proposed an integrated trivariate-dimensional statistical and hydrodynamic modeling approach for assessing the compound flood hazard associated with extreme storm surges, precipitation events, and upstream river discharge. An innovative trivariate copula joint modeling and the frequency amplification method were used to simulate designed values under different return periods (RPs), considering the correlation of the three factors. The results show remarkable differences between the inundated areas of flood events with trivariate drivers and a single driver under the same RPs, indicating that univariate frequency values are insufficient to manage flood threats in compound flood events. The proposed method provides guidelines for comprehending the compound flood process and designing flood control projects in coastal cities. Full article

Cape Canaveral, home to critical space exploration infrastructure, is facing potential flooding hazards from land subsidence and sea-level rise. This study utilized three geodetic datasets, the Interferometric Synthetic Aperture Radar (InSAR), the Global Navigation Satellite System (GNSS), and precise leveling, to investigate the spatial and temporal patterns of vertical land motion (VLM) in Cape Canaveral and its surrounding areas. Our analysis revealed that Cape Canaveral experiences both long-term regional subsidence and localized subsiding areas, while Merritt Island and the Peninsular Mainland remain relatively stable. The long-term regional subsidence in Cape Canaveral is likely driven by the compaction of younger, unconsolidated siliciclastic sediments, with a small contribution from glacial isostatic adjustment (GIA). The three localized subsiding areas identified in Cape Canaveral are each driven by distinct mechanisms: wetland modification in the western area, runway infrastructure development in the central area, and the natural compaction of young siliciclastic sediments in the southeastern region. Historical leveling data indicated temporal variations in subsidence rates at Cape Canaveral, from 5 mm/yr during the 1950–70s to 2 mm/yr in the 2000s. These findings have significant implications for infrastructure resilience and flood hazard assessment, as the observed subsidence compounds with the projected accelerated sea-level rise in the region. Our results highlight the importance of integrating long-term datasets to better characterize VLM in the dynamic coastal region for effective planning and risk mitigation. Full article

Climate change has continued to cause severe coastal flooding, erosion, and storm surge in the northeastern U.S. region. Compounding the coastal storm challenge, this region also experienced multiple 1-in-100-, 1-in-200-, and 1-in-500-year rainfall events in 2024. In recent years, community-based flood risk management has become an important component for generating locally viable mitigation strategies to build environmental sustainability. At the heart of this community engagement paradigm is flood risk communication, which aims to bring together community stakeholders to strengthen their social resilience to collaborate in generating flood risk management solutions. Extant research has rarely examined the direct connection between theory-driven risk communication factors and community-based flood risk management. To better understand the role of risk communication in facilitating participatory flood risk management planning, this study integrated risk communication constructs with the relevant Health Belief Model components to propose and test a conceptual framework. Specifically, this study conducted a survey with 302 residents of a coastal community highly vulnerable to sea level rise, storm surge, and year-round flooding in the coastal region of northeastern U.S. Study results suggested that flood information exposure could drive greater perceived flood risk severity and mitigation barriers, in addition to furthering flood risk information-seeking behavior and affiliated community-engaged flood risk communication. Community-engaged communication was positively linked to perceived social efficacy beliefs in tackling flood risk management, aside from being linked to perceived flood risk mitigation response efficacy. Both perceived social efficacy and response efficacy in flood risk management positively predicted interest in participatory flood risk management planning. Full article

This paper proposes a flood risk visualization method that is (1) readily transferable (2) hyperlocal, (3) computationally inexpensive, and (4) geometrically accurate. This proposal is for risk communication, to provide high-resolution, three-dimensional flood visualization at the sub-meter level. The method couples a laser scanning point cloud with algorithms that produce textured floodwaters, achieved through compounding multiple sine functions in a graphics shader. This hyper-local approach to visualization is enhanced by the ability to portray changes in (i) watercolor, (ii) texture, and (iii) motion (including dynamic heights) for various flood prediction scenarios. Through decoupling physics-based predictions from the visualization, a dynamic, flood risk viewer was produced with modest processing resources involving only a single, quad-core processor with a frequency around 4.30 GHz and with no graphics card. The system offers several major advantages. (1) The approach enables its use on a browser or with inexpensive, virtual reality hardware and, thus, promotes local dissemination for flood risk communication, planning, and mitigation. (2) The approach can be used for any scenario where water interfaces with the built environment, including inside of pipes. (3) When tested for a coastal inundation scenario from a hurricane, 92% of the neighborhood participants found it to be more effective in communicating flood risk than traditional 2D mapping flood warnings provided by governmental authorities. Full article

Long-term impacts of sea-level changes and trends in storm magnitude and frequency along the Mediterranean coasts are key aspects of effective coastal adaptation strategies. In enclosed basins such as a gulf, this requires a step beyond global and regional analysis toward high-resolution modeling of hazards and vulnerabilities at different time scales. We present the compound future projection of static (relative sea level) and dynamic (wind-wave) impacts on the geomorphological evolution of a vulnerable sandy coastal plan located in south Sardinia (west Mediterranean Sea). Based on local temporal trends in H_s (8 mm yr⁻¹) and sea level (5.4 mm yr⁻¹), a 2-year return time flood scenario at 2100 shows the flattening of the submerged morphologies triggering the process of marine embayment. The research proposes adaptation strategies to be adopted to design the projected new coastal area under vulnerabilities at local and territorial scales. Full article

Demak is highly vulnerable to flooding from both fluvial and coastal storms, facing increasing pressures on its sustainability and resilience due to multiple compounding flood hazards. This study assesses the inundation hazards in Demak coastal areas by modeling the impacts of compound flooding. We modeled eight scenarios incorporating long-term forces, such as sea level rise (SLR) and land subsidence (LS), as well as immediate forces, like storm surges, wind waves, and river discharge. Our findings reveal that immediate forces primarily increase inundation depth, while long-term forces expand the inundation area. Combined effects from storm tides and other factors resulted in a 10–20% increase in flood extent compared to individual forces. Fluvial flooding mostly impacts areas near river outlets, but the combination of river discharge and storm tides produces flood extents similar to those caused by SLR. Land subsidence emerged as the primary driver of coastal flooding, while other factors, adding just 25% to area increase, significantly impacted inundation depth. These findings underscore the effectiveness of mangroves in mitigating floods in low-lying areas against immediate forces. However, the resilience and sustainability of the Demak region are challenged by SLR, LS, and the need to integrate these factors into a comprehensive flood mitigation strategy. Full article

Coastal flooding poses a significant threat to coastal communities, adversely affecting both safety and economic stability. This threat is exacerbated by factors such as sea level rise, rapid urbanization, and inadequate coastal infrastructure, as noted in recent climate change reports. Early warning systems (EWSs) have proven to be effective tools in coastal planning and management, offering a high cost-to-benefit ratio. Recent advancements have integrated operational numerical models with machine learning techniques to develop near-real-time EWSs, leveraging data obtained from reputable databases that provide reliable hourly sea-state and sea level data. Despite these advancements, a stepwise methodology for selecting representative events, akin to wave input reduction methods used in morphological modeling, remains undeveloped. Moreover, existing methodologies often overlook the significance of compound extreme events and their potential increased occurrence under climate change projections. This research addresses these gaps by introducing a novel input schematization method that combines efficient hydrodynamic modeling with clustering algorithms. The proposed methodοlogy, implemented in the coastal area of Pyrgos, Greece, aims to select an optimal number of representative sea-state and water level combinations to develop accurate EWSs for coastal flooding risk prediction. A key innovation of this methodology is the incorporation of weights in the clustering algorithm to ensure adequate representation of extreme compound events, also taking into account projections for future climate scenarios. This approach aims to enhance the accuracy and reliability of coastal flooding EWSs, ultimately improving the resilience of coastal communities against imminent flooding threats. Full article

One of the manifests of air-sea interactions is the change in sea level due to meteorological forcing through wind stress and atmospheric pressure. When meteorological conditions conducive to water level increase coincide with high tides during spring tides, the sea level may rise higher than expected and pose a flood risk to coastal low-lying areas. In Hong Kong, specifically when the northeast monsoon coincides with the higher spring tides in late autumn and winter, and sometimes even compounded by the storm surge brought by late-season tropical cyclones (TCs), the result may be coastal flooding or sea inundation. Aiming at forecasting such sea level anomalies on the scale of hours and days with local tide gauges using a flexible and computationally efficient method, this study adapts a data-driven method based on empirical orthogonal functions (EOF) regression of non-uniformly lagged regional wind field from ECMWF Reanalysis v5 (ERA5) to capture the effects from synoptic weather evolution patterns, excluding the effect of TCs. Local atmospheric pressure and winds are also included in the predictors of the regression model. Verification results show good performance in general. Hindcast using ECMWF forecasts as input reveals that the reduction of mean absolute error (MAE) by adding the anomaly forecast to the existing predicted astronomical tide was as high as 30% in February on average over the whole range of water levels, as well as that compared against the Delft3D forecast in a strong northeast monsoon case. The EOF method generally outperformed the persistence method in forecasting water level anomaly for a lead time of more than 6 h. The performance was even better particularly for high water levels, making it suitable to serve as a forecast reference tool for providing high water level alerts to relevant emergency response agencies to tackle the risk of coastal inundation in non-TC situations and an estimate of the anomaly contribution from the northeast monsoon under its combined effect with TC. The model is capable of improving water level forecasts up to a week ahead, despite the general decreasing model performance with increasing lead time due to less accurate input from model forecasts at a longer range. Some cases show that the model successfully predicted both positive and negative anomalies with a magnitude similar to observations up to 5 to 7 days in advance. Full article

Many cities worldwide are increasingly threatened by compound floods resulting from the interaction of multiple flood drivers. Simultaneously, rapid urbanization in coastal areas, which increases the proportion of impervious surfaces, has made the mechanisms and simulation methods of compound flood disasters more complex. This study employs a comprehensive literature review to analyze 64 articles on compound flood risk under climate change from the Web of Science Core Collection from 2014 to 2024. The review identifies methods for quantifying the impact of climate change factors such as sea level rise, storm surges, and extreme rainfall, as well as urbanization factors like land subsidence, impervious surfaces, and drainage systems on compound floods. Four commonly used quantitative methods for studying compound floods are discussed: statistical models, numerical models, machine learning models, and coupled models. Due to the complex structure and high computational demand of three-dimensional joint probability statistical models, along with the increasing number of flood drivers complicating the grid interfaces and frameworks for coupling different numerical models, most current research focuses on the superposition of two disaster-causing factors. The joint impact of three or more climate change-driving factors on compound flood disasters is emerging as a significant future research trend. Furthermore, urbanization factors are often overlooked in compound flood studies and should be considered when establishing models. Future research should focus on exploring coupled numerical models, statistical models, and machine learning models to better simulate, predict, and understand the mechanisms, evolution processes, and disaster ranges of compound floods under climate change. Full article

The effects of climate change in the forms of rising sea levels and increased frequency of storms and storm surges are being noticed across many coastal communities around the United States. These increases are impacting the timing and frequency of tidal and rainfall influenced compound groundwater flooding events. These types of events can be exemplified by the recent and ongoing occurrence of groundwater flooding within building basements at the historic Strawbery Banke Museum (SBM) living history campus in Portsmouth, New Hampshire. Fresh water and saline groundwater intrusion within basements of historic structures can be destructive to foundations, mortar, joists, fasteners, and the overlaying wood structure. Although this is the case, there appears to be a dearth of research that examines the use of wireless streaming sensor networks to monitor and assess groundwater inundation within historic buildings in near-real time. Within the current study, we designed and deployed a three-sensor latitudinal network at the SBM. This network includes the deployment and remote monitoring of water level sensors in the basements of two historic structures 120 and 240 m from the river, as well as one sensor within the river itself. Groundwater salinity levels were also monitored within the basements of the two historic buildings. Assessments and model results from the recorded sensor data provided evidence of both terrestrial rainfall and tidal influences on the flooding at SBM. Understanding the sources of compound flooding within historic buildings can allow site managers to mitigate better and adapt to the effects of current and future flooding events. Data and results of this work are available via the project’s interactive webpage and through a public touchscreen kiosk interface developed for and deployed within the SBM Rowland Gallery’s “Water Has a Memory” exhibit. Full article

China’s vulnerability to fluvial floods necessitates extensive exposure studies. Previous large-scale regional analyses often relied on a limited set of assessment indicators due to challenges in data acquisition, compounded by the scarcity of corresponding large-scale flood distribution data. The integration of public datasets offers a potential solution to these challenges. In this study, we obtained four key exposure indicators—population, built-up area (BA), road length (RL), and average gross domestic product (GDP)—and conducted an innovative analysis of their correlations both overall and locally. Utilising these indicators, we developed a comprehensive exposure index employing entropy-weighting and k-means clustering methods and assessed fluvial flood exposure across multiple return periods using fluvial flood maps. The datasets used for these indicators, as well as the flood maps, are primarily derived from remote sensing products. Our findings indicate a weak correlation between the various indicators at both global and local scales, underscoring the limitations of using singular indicators for a thorough exposure assessment. Notably, we observed a significant concentration of exposure and river flooding east of the Hu Line, particularly within the eastern coastal region. As flood return periods extended from 10 to 500 years, the extent of areas with flood depths exceeding 1 m expanded markedly, encompassing 2.24% of China’s territory. This expansion heightened flood risks across 15 administrative regions with varying exposure levels, particularly in Jiangsu (JS) and Shanghai (SH). This research provides a robust framework for understanding flood risk dynamics, advocating for resource allocation towards prevention and control in high-exposure, high-flood areas. Our findings establish a solid scientific foundation for effectively mitigating river flood risks in China and promoting sustainable development. Full article

Drawing on a case study from Ternate Island, a densely populated volcanic island in Eastern Indonesia, this research illustrates how multi-hazards and extreme weather events are likely to compound and cascade, with serious consequences for sustainable development in small island context. At the heart of Ternate Island sits the active Gamalama volcano, posing a constant eruption threat. Its location within the Ring of Fire further exposes the island to the risks of tsunamis and earthquakes. Additionally, the island’s physical features make it highly susceptible to flooding, landslides, and windstorms. Rapid urbanization has led to significant coastal alterations, increasing exposure to hazards. Ternate’s small-island characteristics include limited resources, few evacuation options, vulnerable infrastructure, and inadequate resilience planning. Combining GIS multi-hazard mapping with a structured survey in 60 villages in Ternate, this case study investigates the multi-hazard exposure faced by the local population and land coverage. The findings suggest significant gaps between village chiefs’ perceptions of the types of hazards and the multi-hazard assessment in each village. Out of 60 village chiefs surveyed, 42 (70%) are aware of earthquake risks, 17 (28%) recognize tsunami threats, and 39 see volcanoes as a danger. GIS assessments show that earthquakes could impact all villages, tsunamis could affect 46 villages (77%), and volcanoes could threaten 39 villages. The hazard map indicates that 32 villages are at risk of flash floods and 37 are at risk of landslides, and extreme weather could affect all villages. Additionally, 42 coastal villages on Ternate Island face potential extreme wave and abrasion disasters, but only 18 chiefs acknowledge extreme weather as a threat. The paper argues that addressing the cognitive biases reflected in the perceptions of community leaders requires transdisciplinary dialogue and engagement. Full article