Dam-break waves are highly unsteady long-wave phenomena, characterized by a breaking front with a strong recirculating air–water mixture. While the air–water flow properties of steady flows have often been investigated, the understanding of dynamic processes in unsteady multiphase flows remains limited. In this experimental study, a new approach was implemented to analyze the air–water flow properties of highly unsteady flows in the form of dam-break waves using ensemble-averaging techniques to account for short-duration measurements. The new dataset includes four different flow conditions, providing novel insights into the relation between various hydrodynamic characteristics and key air–water flow properties, including bubble characteristics and void fraction. The void fraction profiles indicated the presence of a turbulent shear layer along with a recirculation zone close to the free surface, showing analogies with similar steady and unsteady flow phenomena. Variations in the Froude number were shown to strongly affect the number and size of air bubbles, particularly in the shear layer. Higher depth-averaged air concentrations were found with increasing Froude numbers, reaching up to 40% for Fr = 5.14. Overall, the results confirm the importance of considering the presence of air in dam-break waves and demonstrate the suitability of this new methodology for investigating air–water flow properties in highly turbulent flows. They offer a deeper understanding of the multiphase nature of dam-break waves, which is relevant for a wide range of processes in coastal and hydraulic engineering.

Land subsidence is a significant issue in many coastal megacities, including Shanghai, where it poses risks to infrastructure and economic stability. Although numerous studies have used SAR datasets to monitor land subsidence in Shanghai, multi-decadal displacement measurements obtained from multi-sensor SAR data remain unavailable. Moreover, the contributions and variations of driving factors behind the evolution of land subsidence remain poorly understood. This study employs multi-sensor SAR fusion method and a Random Forest model, along with Shapley Additive exPlanations (SHAP), to examine subsidence evolution and assess the influence of key drivers over the past 30 years. The results show that severe subsidence has spread from central urban areas to surrounding suburban regions, particularly in the eastern coastal and southern industrial zones in Shanghai. SHAP analysis identified that evapotranspiration, sediment thickness, and groundwater extraction were the dominant factors in the early stage of subsidence, while recent groundwater management and recharge practices have significantly mitigated the subsidence rate. These findings demonstrate the shifting importance of different subsidence factors over time and provide valuable insights for long-term prevention and control measures.

Deep-learning-based surrogate models represent a powerful alternative to numerical models for speeding up flood mapping while preserving accuracy. In particular, solutions based on hydraulic-based graph neural networks (SWE-GNNs) enable transferability to domains not used for training and allow the inclusion of physical constraints. However, these models are limited due to four main aspects. First, they cannot model rapid differences in flow propagation speeds; secondly, they can face instabilities during training when using a large number of layers, needed for effective modelling; third, they cannot accommodate time-varying boundary conditions; and fourth, they require initial conditions from a numerical solver. To address these issues, we propose a multi-scale hydraulic-based graph neural network (mSWE-GNN) that models the flood at different resolutions and propagation speeds. We include time-varying boundary conditions via ghost cells, which enforce the solution at the domain’s boundary and drop the need for a numerical solver for the initial conditions. To improve generalization over unseen meshes and reduce the data demand, we use invariance principles and make the inputs independent from coordinates' rotations. Numerical results applied to dike-breach floods show that the model predicts the full spatio-temporal simulation of the flood over unseen irregular meshes, topographies, and time-varying boundary conditions, with mean absolute errors in time of 0.05 m for water depths and 0.003 m2 s−1 for unit discharges. We further corroborate the mSWE-GNN in a realistic case study in the Netherlands and show generalization capabilities with only one fine-tuning sample, with mean absolute errors of 0.12 m for water depth, a critical success index for a water depth threshold of 0.05 m of 87.68 %, and speed-ups of over 700 times. Overall, the approach opens up several avenues for probabilistic analyses of realistic configurations and flood scenarios.

Introduction: The 2021 European floods in Germany, Belgium, and the Netherlands significantly impacted healthcare. With climate change increasing flood risks, healthcare preparedness is essential. Floods affect healthcare directly and indirectly by disrupting patient access, damaging infrastructure and impeding care continuity. Our interdisciplinary research in the Netherlands systematically assesses flood impacts on healthcare, optimises disaster preparedness, patient logistics, and continuity and explores crisis governance, incorporating lessons from coronavirus disease-2019 (COVID-19). Methods: Our multi-sited, interdisciplinary project titled “Pandemic lessons for flood disaster preparedness” includes literature reviews on: (i) the (in) direct impacts of floods on healthcare, (ii) disaster decision-making strategies and (iii) patient logistics during crises. Empirically, ethnographic methods (interviews, focus groups, document analyses, and observations) will: (a) assess hospital flood preparedness, (b) explore decision-making and crisis management strategies and (c) analyse the dynamics of health system governance during floods. Data from these sources and flood scenarios will inform models on healthcare impacts and decision-making, culminating in a simulation game for research and training. Discussion: This study offers a comprehensive, interdisciplinary approach to understanding and improving healthcare system preparedness for floods. By integrating diverse fields such as healthcare governance, disaster risk management, logistics and hydraulic engineering, we provide a unique lens on resilience. A key strength is the incorporation of lessons from the COVID-19 pandemic, allowing us to draw parallels between pandemic response and flood preparedness. In addition, our simulation game serves as a robust tool for translating knowledge into practice. However, the study’s reliance on collaboration with busy healthcare and disaster response professionals may limit engagement. Moreover, the absence of direct public and patient involvement in the research design, though partially mitigated by engaging representative organizations, presents a potential limitation. Lastly, the challenge of obtaining real-time data from flood events could introduce recall bias, but triangulation of various data sources aims to address this issue. Despite these challenges, the study’s integration of long-term data from recent floods and focus on healthcare-specific crisis governance provides valuable insights for improving disaster preparedness.

Hydrometeorological hazards, particularly floods and cyclones, pose significant threats to human health, including fatalities, damage to healthcare facilities (HCFs), and disruptions to health services. This study systematically reviewed scientific articles to identify the direct and indirect impacts of floods on HCFs and the risk management strategies implemented to address these challenges. To that end, we searched four databases (MEDLINE, Embase, Web of Science and Scopus) for articles written in English. Our search query included terms related to flood and cyclone hazards, HCF types and disaster risk management strategies. We followed the PRISMA guidelines to conduct the study. The search resulted in 7500 records, which were finally filtered down to 74 studies after removing duplicates, screening records and full article eligibility checks. Approximately 76 % of the included studies addressed cyclone-related flood impacts and were conducted in the United States. Hospitals were the most studied HCFs (n = 54) followed by long-term care facilities (n = 11). The main impact of floods on hospitals was due to flooded basements as they house important services including equipment, supplies and backup generators. Interruptions of electricity and water supplies were reported to cause serious challenges. Regarding flood risk management, patient evacuation was mentioned by more than 66 % of the studies while few studies reported the implementation of structural measures. More than a third of the studies reported the availability of preparedness plans. The review revealed inconsistencies in the flood preparedness of HCFs. The main policy recommendations are the availability of guidelines to standardize preparedness plans and oversight.

Rock groins in the Elbe Estuary are constructed to maintain proper water levels for navigation and for embankment erosion protection. At certain localities, significant damages to rock groins have been observed due to the primary ship-generated waves. Primary waves are generated along the ship's hull and then propagate toward the river banks and groin fields, appearing in the interaction with the structures as a turbulent overflow phenomenon. Eventually, this overflowing may cause damages mainly to the crest and leeward side of the groins. Since this overflowing is the most pronounced with large primary waves at certain water levels, the estimation of the probabilities of extreme primary waves is a key element for a safe and reliable design of groins. For this goal, nonparametric Bayesian networks (NPBNs) are used here to infer the probability distribution function of the extreme primary wave heights at the tip of a groin in the Elbe Estuary. Results demonstrate the suitability of the NPBN in their prediction. The model framework allows the designer to predict the probabilities of primary ship-generated waves at groins when the information of ship dimensions, nautical parameters, and waterway geometry is available. These probabilities can later be used for design purposes for current and future conditions.

During extreme high-water events in river systems, the load on a levee section may exceed its resistance, initiating the breaching process which eventually leads to levee failure. The success of an emergency measure to intervene in the initial phases of levee failure is mainly dependent on its timely application. Quick action is required to prepare and deploy an emergency measure before damages to the levee section have become irreparable. In this study, we investigate the key parameters for successful application of an emergency measure, focusing on the BresDefender case study. The BresDefender is a floating pontoon used by the Dutch military, which is intended to avoid or postpone levee failure. A model has been developed taking the operational steps in the implementation of the emergency measure during a high water and the (uncertainty) in the duration of these processes into account. The model is used to quantify the probability of successful operation to prevent levee failure due to overflow or slope instability. The probability of successful application of the BresDefender has been simulated for river flood situations in the Netherlands. For the river Rhine, where the examined cases were prone to slope instabilities, the probability of arriving in time was found to be 70%. But for the Meuse case, where the examined cases were prone to overflow, the probability of arriving in time was found to be only 0%. The critical steps in the process after the occurrence of damage to the levee are damage detection, the decision to repair the damage, the transport of the emergency measure, and the placement of the measure. By incorporating emergency measures in emergency preparedness procedures, the time required for the critical steps will be decreased and the probability of successful application of the emergency measure, i.e., its contribution to flood risk reduction, will be enhanced.

During extreme high-water events, the phreatic water level in levees will rise over time due to infiltration of water. This can promote slope instability or internal erosion, and eventually lead to structural failure. A potential solution is the application of an impermeable seal, such as a geotextile, to the levee’s outer slope to locally reduce the inflow of water. In this study, the spatiotemporal effect of a seal on the phreatic surface level is investigated experimentally, both at laboratory scale for a homogeneous sand levee, and at full-scale for a more realistic levee design. On the two-dimensional laboratory scale, it was found that application of a seal does not significantly change the steady-state phreatic level, as expected from a theoretical perspective. However, the time for the phreatic surface level to reach steady state after a sudden external water rise was found to increase 25% to 50% in the cases with a seal. Similar results were found for the full-scale three-dimensional experiments, which showed that details of the soil-structure interface significantly influenced the effectiveness of the impermeable seal, increasing the time to steady state between 12% and 25%. A simple numerical transient groundwater flow model confirms that the quality of the seal governs the response of the phreatic level. This model required the inclusion of an interface layer to properly model the imperfect soil-seal conditions. It is concluded that application of an impermeable seal to a levee before sudden water rise does not influence the new steady-state phreatic level. However, the seal slows down the infiltration process, especially for a case where the outer slope is damaged.

Immersed tunnels are positive buoyant structures during installation and negative buoyant after installation. A tunnel is composed of sequential immersed elements that are coupled to each other in joints. Tunnel elements consist of segments which are compressed to each other by longitudinal post-tensioning. After immersion the tunnel is supported by the seabed and the longitudinal post-tension is cut at the joints between segments. Therefore, the structure is a segmented lining which is sensitive for settlements due to non uniform circumstances over the length of the tunnel. An uneven response of the bedding underneath the tunnel introduce shear forces in joints of an immersed tunnel. Because immersed tunnels need to be buoyant during installation, they have limitations on weight and geometry, the size and therefore the capacity of these shear keys is limited because the height of the tunnel, as shear keys are applied in the walls of the tunnel. The foundation response is influenced by many factors related to subsoil but also to construction and dredging tolerances. The shear forces were derived as a function of different covariance lengths for subsoil stiffness and dredging tolerances for different tunnel layouts. In reliability analyses, using two different probabilistic methods, exceedance probabilities of maximum shear forces are derived for one lay out using Non Parametric Bayesian Networks and Vine Copulas. The analyses give more insight in to the magnitude of the shear forces in joints both in conditioned and unconditioned situations and this can be used for the design of immersed tunnels.

Internal erosion is a significant cause of failure in dams, levees and other hydraulic structures. This article studies the time-dependent reliability of such structures under Backward Erosion Piping (BEP), a form of internal erosion in the foundation. First, a physics-based time-dependent piping failure model is presented. Second, a time-variant reliability analysis method is presented which allows to quantify how the reliability evolves over the years due to cumulative pipe growth over multiple flood events. Finally, these models are used to study the importance of time-dependence for reliability estimates of flood defenses in The Netherlands. The findings show that, particularly in coastal areas, incorporating time-dependence significantly reduces the computed failure probability. Reductions vary widely, ranging from a factor of 5 to more than (Formula presented.) depending on flood duration and levee properties. Therefore, reliability estimates for levees can be improved by incorporating time-dependent pipe development in the BEP failure model, and thereby contribute to avoiding unnecessary reinforcements.

Sea level rise necessitates the upgrade of coastal flood protection including storm surge barriers. These large movable hydraulic structures are open in normal conditions, but close during a storm surge to prevent coastal floods in bays and estuaries. Barrier improvements lower their susceptibility to operational, structural, or height-related failures. However, there is no method to determine the relative importance of these three barrier failure types. Here, we present a probabilistic method to systematically organize barrier failures and storm conditions to establish exceedance frequencies of extreme water levels behind the barrier. The method is illustrated by an assessment of extreme water level frequencies at Rotterdam (The Netherlands), which is protected by the Maeslant barrier. Four combinations of barrier states and storm conditions were analyzed and prioritized in the following order: (1) an operational failure with 1/100 year storm conditions, (2) a successful closure with an extreme (~1/1000 year) river discharge accumulating behind the barrier, (3) structural failure, and (4) insufficient height both with extreme storm conditions (10^–6 year). The case study confirmed the method's ability to systematically explore promising barrier improvements to adapt to sea level rise, in this case, lowering the susceptibility toward operational failures.

Floods are amongst the most frequent disasters in terms of human and economic impacts. This study provides new insights into the frequency of loss of life at the global scale, mortality fractions of the population exposed to floods, and underlying trends. A dataset is compiled based on the EM-DAT disaster database covering the period 1975 until 2022, extending previous studies on this topic. Flood impact data are analysed over spatial, temporal and economic scales, decomposed in various flood types and compared with other natural disasters. Floods are the most frequent natural disasters up to 1000 fatalities, and flash floods lead to the highest mortality fractions per event, i.e. the number of deaths in an event relative to the exposed population. Despite population growth and increasing flood hazards, the average number of fatalities per event has declined over time. Mortality fractions per event have decreased over time for middle- and high-middle-income countries, but increased for low-income countries. This highlights the importance of continuing and expanding risk reduction and adaptation efforts.

In the face of increasing uncertainties and the expected rise in flood events worldwide, this technical note presents an innovative approach for enhancing rapid response capabilities, exemplified during the Kakhovka Dam breach in Ukraine. Utilizing the Tygron Model, our methodology combines open-source data with high performance computing for swift hydrodynamic simulations—a departure from traditional flood risk management techniques. Central to this approach is the strategic use of social media to gather crowd-sourced feedback during the emergency, enhancing the precision and relevance of flood risk information.

During the Kakhovka Dam event, our model processed extensive datasets, enabling effective predictions of flood impacts, including extents, velocities, depths, and arrival times. The near real-time modeling capability allowed for dynamic updates using social media inputs, which were of value for emergency responders to optimize response strategies for relief coordination.

While the underlying technology is used for flood simulations, its application in emergency response is novel and promising for more adequate disaster response coordination. However, further research and applications are necessary to refine the approach that can ensure real-time flood risk information during a emergency situations.

Coastal regions have experienced significant environmental changes and increased vulnerability to floods caused by the combined effect of multiple flood drivers such as storm surge, heavy rainfall and river discharge, i.e., compound floods. Hence, for a sustainable development of coastal cities, it is necessary to understand the spatiotemporal dynamics and future trends of compound flood hazard. While the statistical dependence between flood drivers, i.e., rainfall and storm surges, has been extensively studied, the sensitivity of the inundated areas to the relative timing of a driver's individual peaks is less understood and location dependent. To fill this gap, here we propose a framework combining a statistical dependence model for compound event definition and a hydrodynamic model to assess inundation maps of compound flooding from storm surge and rainfall during typhoon season in Shanghai. First, we determine the severity of the joint design event, i.e., peak surge and precipitation, based on the copula model. Second, we use the same frequency amplification (SFA) method to transform the design event values in hourly time series so that they represent boundary conditions to force hydrodynamic models. Third, we assess the sensitivity of inundation maps to the time lag between storm surge peak and rainfall. Finally, we define flood zones based on the primary flood driver, and we delineate flood zones under the worst compound flood scenario. The study highlights that the temporal delay between storm surge and rainfall plays a pivotal role in shaping the dynamics of flooding events. More specifically, that the peak rainfall occurs 2 h before the peak storm surge would cause the deepest average cumulative inundation depth. At the same time, the results show that in Shanghai surge is the primary flood driver. High storm surge at the eastern part of the city (Wusongkou tidal gauge) propagates upstream in the Huangpu River, resulting in fluvial flooding in Shanghai city center and several surrounding districts. This calls for a better fluvial flooding control system hinging on the backwater effect during high surge in the upper and middle Huangpu River and in the newly added urbanized areas to ensure flood resilience. The proposed framework is useful to evaluate and predict flood hazard in coastal cities, and the results can provide guidance for urban disaster prevention and mitigation.

Mangrove forests reduce wave attack along tropical and sub-tropical coastlines, decreasing the wave loads acting on coastal protection structures. Mangrove belts seaward of embankments can therefore lower their required height and decrease their slope protection thickness. Wave reduction by mangroves depends on tree frontal surface area and stability against storms, but both aspects are often oversimplified or neglected in coastal protection designs. Here we present a framework to evaluate how mangrove belts influence embankment designs, including mangrove growth over time and failure by overturning and trunk breakage. This methodology is applied to Sonneratia apetala mangroves seaward of embankments in Bangladesh, considering forest widths between 10 and 1000 m (cross-shore). For water depths of 5 m, wave reduction by mangrove forests narrower than 1 km mostly affects the slope protection and the bank erodibility, whereas the required embankment height is less influenced by mangroves. Sonneratia apetala trees experience a relative maximum in wave attenuation capacity at 10 years age, due to their large submerged canopy area. Once trees are more than 20 years old, their canopy is emergent, and most wave attenuation is caused by trunk and roots. Canopy emergence exposes mangroves to wind loads, which are much larger than wave loads, and can cause tree failure during cyclones. These results stress the importance of including tree surface area and stability models when predicting coastal protection by mangroves.

The historic canal walls of Amsterdam, stretching 200 km in total, are constructed as a masonry wall on a timber deck supported by vertical timber piles. Understanding the resistance against lateral failure of these quays has been challenging due to uncertainties in their working principles, geometry, soil and structural properties. This paper proposes a Bayesian approach to include evidence from past loading situations and corresponding deformations into the reliability assessment. This approach enables refinement of the reliability predictions and parameter distribution uncertainties, leading to a more accurate prediction of the resistance against the lateral failure of historic quay wall. Depending on the type of evidence, an a-priori reliability prediction for a quay wall that fails to meet safety standards can be updated to any of the three consequence classes outlined in NEN8700. In a case study, a quay wall with an a-priori reliability of β = 1.5 has been increased to β = 3.2 by including evidence of an extreme survived load of 10 kN/m² that resulted in displacements of less than 4 mm. This is a decrease in failure probability by two orders of magnitude, showing the potential impact of using observational information in combination with Bayesian updating.

The authors provide a valuable review of coastal defense megaprojects with storm surge barriers in the United States over the past decades. This review comes at an important moment: an increasing number of coastal regions are considering such solutions. Examples from the original paper are Houston, Miami, New York, Boston, and Norfolk in the US, Götenborg (Sweden), Shanghai, and Singapore (Tan 2022). A new barrier is under construction in Nieuwpoort (Belgium) (AMDK 2023). In addition, countries with existing barriers (the Netherlands, UK) are making plans to adapt, upgrade or replace surge barriers.

Floods are consistently ranked as the most financially devastating natural disasters worldwide. Recent flood events in the Netherlands, Caribbean, and US have drawn attention to flood risks resulting from pluvial and fluvial sources. Despite shared experiences with flooding, these regions employ distinct approaches and flood management strategies due to differences in governance and scale—offering a three-site case study comparison. A key, yet often lacking, factor for flood risk and damage assessments at the parcel level is building elevation compared to flood elevation. First-floor elevations (FFEs) are a critical element in the vulnerability of a building flooding. US-based flood insurance policies require FFEs; however, data availability limitations exist. Drone-based FFEs were measured in all locations to assess the flood vulnerabilities of structures. Flood vulnerability profiles revealed 64% of buildings were vulnerable to a form of inundation, with 40% belonging to “moderate” or “major” inundation, and inundation elevation means (IEMs) of −0.55 m, 0.19 m, and 0.71 m within the US, Netherlands, and Puerto Rico sites, respectively. Spatial statistics revealed FFEs were more responsible for flood vulnerabilities in the US site while topography was more responsible in the Netherlands and Puerto Rico sites. Additional findings in the Puerto Rico site reveal FFEs and next highest floor elevations (NHFEs) vulnerable to future sea level rise (SLR) flood elevations. The findings within the Netherlands provide support for developing novel multi-layered flood risk reduction strategies that include building elevation. We discuss future work recommendations and how the different sites could benefit significantly from strengthening FFE requirements.

Canal dikes in low-lying polders, as well as in other regions worldwide, are critical infrastructure for flood protection and water management. The subsurface water conditions can cause dike failures during excessive rainfall and prolonged periods of drought. There is a lack of multi-year monitoring of subsurface water conditions in canal dikes and an insufficient understanding of their geohydrological behavior. This study provides and analyses a novel multiyear data set of soil moisture and hydraulic heads (from February 2020 until March 2023) from a monitoring network covering various canal dikes with different characteristics in the western Netherlands. The data, including two extremely dry summers, highlight the impact of meteorological variations on the subsurface water conditions. Non-hydrostatic hydraulic head levels were observed during droughts that can be detrimental to dike stability and that are often not accounted for in safety assessments for drought situations. The effectiveness of various meteorological drought indicators applied to subsurface water conditions was evaluated: the precipitation deficit is the most reliable measure and outperforms the standardized drought indicators (SPEI and SPI). The drought recovery of dikes was analyzed to understand seasonal transitions and the sequence of different failure mechanisms, during dry and wet situations. This analysis also reveals differences between meteorological, soil moisture, and groundwater droughts, highlighting soil's storage capacity after drought and the limitations of meteorological drought indicators as proxies for soil moisture and groundwater. The insights from this study enhance assessments, inspection procedures and the identification of weak spots of dikes and other earthworks of infrastructure.

Coastal defenses must be upgraded to combat increasing flood risk due to climate change and other factors. Storm surge barriers, large movable hydraulic structures that close temporarily during storm surges to prevent coastal floods, play a vital role in protecting estuaries. Due to the complexity of their risk analyses, important improvements are sometimes overseen. Our objective is to develop a systematic approach which is more likely to find these important improvements. We tested the method to three historic cases where important improvements were initially overlooked. We anticipate that our method can be applied to other safety systems with a large number of failure modes as well.