Pluvial Flooding Research Articles

Hazards of a flooding event in the city of Gdansk and possible forms of preventing the phenomenon – case study

ABSTRACT The main objective is to examine the urban flood hazard in the city of Gdansk and to determine the possibilities of preventing this phenomenon. Hydrological and hydraulic modeling was used for the case study analysis of urban flood in Strzyża basin, applying the HEC-HMS and HEC-RAS systems. The result of modeling with the assumption of torrential rainfall with a duration of t = 1 h (from 35 to 58 mm) is the probability of pluvial flood occurrence divided into low probability (p = 10%), high probability (p = 1%) and catastrophic probability (p = 0.2%). The normal flow of the Strzyża stream at the mouth is 0.175 m3·s−1, while the modeling results indicate an increase in the flow depending on the assumed flood flow probability from about 41.8 m3·s−1 to 54.7 m3·s−1. In addition, the article presents the basic elements of the policy of the city of Gdańsk in the field of reducing the hazards of urban flooding.

Risk assessment for hurricane-induced pluvial flooding in urban areas using a GIS-based multi-criteria approach: A case study of Hurricane Harvey in Houston, USA

As one of the most destructive nature hazards, hurricane-induced flooding generates serious adverse impacts on populations, infrastructure, and the environment globally. In urban areas, complex characteristics such as high population and infrastructure densities increase flood disaster risks. Consequently, the assessment of flood risks is becoming increasingly important for understanding potential impacts on an urban area and proposing disaster risk mitigation strategies. After conducting a comprehensive literature review, this study finds that most urban flood risk assessments often overlook urban ecosystem elements, focusing more on social and economic aspects. Hence, the role of urban ecosystems cannot be fully understood. To address this gap, this study proposes a social-ecological systems (SES) flood risk assessment framework for urban areas. Based on this framework, a comprehensive list of indicators collected through a literature review is provided for urban flood risk assessments. A comparative study of flood risk during Hurricane Harvey (2017) in Houston, Texas, USA, is carried out using the improved analytic hierarchy process (IAHP) weighting method and the equal weighting method for indicator weighting. Results are then compared with the damage data of Hurricane Harvey published by the U.S. Federal Emergency Management Agency (FEMA). The analysis identifies that the western part of Houston had the highest flood risks, while the center of Houston was at lower flood risk. Comparisons between the results from the IAHP and equal weighting methods show that the latter produces a broader range of high flood risk areas than the former. This study also highlights the role of urban ecosystems in mitigating flood risks and advocates for more holistic, social-ecological assessments of flood risk. Such assessments could utilize the proposed framework and the indicator list but contextualize these to the specific urban area's contexts being investigated.

EXTREME RAINFALL-RUNOFF MODELING DURING REMNANTS OF IDA IN NEW YORK

While the effects of wind and pressure on coastal waters can generate large storm surges that lead to coastal flooding, storm surge represents only one driver of flooding. In reality, flooding is often driven by the combined action of several geophysical drivers (Couasnon et al., 2020; Zscheischler et al., 2018). Formally compound flooding is defined as the combined action of meteorological forces, tidal forces,, fluvial, and pluvial discharge (Zscheischler et al., 2018). Recent events such as the extreme rainfall associated with the remnants of Hurricane Ida in the Northeast United States have demonstrated the need to model the rainfall-runoff impact (i.e., pluvial flooding) and its downstream impact (i.e., fluvial flooding). In extreme rainfall cases in which large quantities of precipitation fall in short periods, urban environments often flood, and wastewater collection systems often exceed their design limit. In severe scenarios, surcharged sewer systems can significantly exacerbate urban flooding. While several existing flood risk methods consider storm surge in them approach, it remains active research topic concerning how and when rainfall-runoff processes should be incorporated in flooding models to assess site-specific flood risk. Often an approach is pursued that involves loosely coupling multiple numerical models each modeling a particular flood driver; however, this approach can become cumbersome. Instead in this work we pursue a different approach in which compound flooding can be resolved in a single numerical model (herein termed a monolithic approach).

MULTI-HAZARD RISK ASSESSMENT FOR TONGATAPU – A COASTAL INUNDATION LENS

A multi-hazard disaster risk assessment was undertaken for Tongatapu through the Asian Development Bank (ADB) on behalf of the Government of Tonga. This was to inform investments, urban planning and improvements to natural hazard resilience across the island (ADB, 2021). This paper presents the methodology and findings of the coastal inundation and sea level rise components and how these relate to other hazards assessed and the overarching risk assessment. Due to its low elevation and flat topography, Tongatapu, and the Tongan capital of Nukualofa, are vulnerable to pluvial (surface) flooding associated with heavy rainfall and coastal flooding from extreme sea levels and cyclone induced storm surge. Similarly, Tongatapu is vulnerable to damaging winds associated with tropical cyclones, earthquakes particularly associated with the nearby Kermadec Tonga Trench, and tsunami inundation. The study assessed these hazards and considered the influence of climate change on intensity of rainfall and sea levels.

Machine learning approach for modeling daily pluvial flood dynamics in agricultural landscapes

Machine learning approach for modeling daily pluvial flood dynamics in agricultural landscapes

Developing the Actual Precipitation Probability Distribution Based on the Complete Daily Series

The defense against urban pluvial flooding relies on the prediction of rainfall frequency, intensity, and long-term trends. The influence of the choice of the complete time series or the wet-day series on the rain analyses remains unclear, which affects the adaptive strategies for the old industrial cities such as Changchun in Northeastern China, with the outdated combined sewer systems. Based on the data from the two separate weather stations, four types of distributions were compared for analyzing the complete daily precipitation series, and their fitting accuracy was found in decreasing order of Pearson III, Pareto–Burr–Feller distribution (PBF), generalized extreme value (GEV), and Weibull. The Pearson III and the PBF probability distribution functions established based on the complete time series were found to be at least 458% and 227%, respectively, more accurate in fitting with the consecutive observations than those built from the wet-day-only series, which did not take account of the probability of the dry periods between the rain events. The rain depths of the return periods determined from the wet-day-only series might be over-predicted by at least 76% if the complete daily series were regarded as being more closely representative of the real condition. A clear threshold of 137 days was found in this study to divide the persistent or autocorrelated time series from the antipersistent or independent time series based on the climacogram analysis, which provided a practical way for independence determination. Due to the significant difference in the rain analyses established from the two time series, this work argued that the complete daily series better represented the real condition and, therefore, should be used for the frequency analysis for flood planning and infrastructure designs.

Evaluating the association between morphological characteristics of urban land and pluvial floods using machine learning methods

Spatial pattern of urban land plays a significant role in the occurrence of pluvial floods. While landscape metrics have been widely used to reflect land use spatial patterns, the association between the morphological spatial pattern of urban land and pluvial floods is much less understood. Unlike landscape metrics, morphological spatial pattern analysis could discover more spatial information on the formation and layout of land use. To fill such knowledge gap, this research evaluated whether the morphological characteristics of urban land matter to pluvial floods based on machine learning techniques. Pearson's correlation test and random forest algorithm were employed to discover the linear and non-linear associations between the density of flood hotspots and a set of underlying influencing factors, respectively. A case study in a low-lying coastal city has highlighted the strong influence of land use morphological characteristics on pluvial floods with baseline influencing factors considered. The density of flood hotspots was positively associated with the core, loop, edge, and bridge of urban land at sub-watershed level, but negatively associated with islet urban land. While it is unfeasible to restrain rapid urban sprawl and population growth in many developing countries, this research could give specific guidance for upgrading the morphological spatial pattern of urban land. A proper design of key morphological elements may overcome the negative influence of urban expansion on pluvial floods. These findings could help urban planners and policy makers to highlight the importance of land use morphological spatial patterns.

Urban Flood Modelling under Extreme Rainfall Conditions for Building-Level Flood Exposure Analysis

The expansion of urban areas and the increasing frequency and magnitude of intense rainfall events are anticipated to contribute to the widespread escalation of urban flood risk across the globe. To effectively mitigate future flood risks, it is crucial to combine a comprehensive examination of intense rainfall events in urban areas with the utilization of detailed hydrodynamic models. This study combines extreme value analysis techniques applied to rainfall data ranging from sub-hourly to daily durations with a high-resolution flood modelling analysis at the building level in the centre of Thessaloniki, Greece. A scaling procedure is employed to rainfall return levels assessed by applying the generalised extreme value (GEV) distribution to annual maximum fine-temporal-scale data, and these scaling laws are then applied to more reliable daily rainfall return levels estimated by means of the generalised Pareto distribution (GPD), in order to develop storm profiles with durations of 1 h and 2 h. The advanced flood model, CityCAT, is then used for the simulation of pluvial flooding, providing reliable assessments of building-level exposure to flooding hazards. The results of the analysis conducted provide insights into flood depths and water flowpaths in the city centre of Thessaloniki, identifying major flowpaths along certain main streets resulting in localised flooding, and identifying around 165 and 186 buildings highly exposed to inundation risk in the study area for 50-year storm events with durations of 1 h and 2 h, respectively. For the first time in this study area, a detailed analysis of extreme rainfall events is combined with a high-resolution Digital Terrain Model (DTM), used as an input into the advanced and fully featured CityCAT hydrodynamic model, to assess critical flowpaths and buildings at high flood risk. The results of this study can aid in the planning and design of resilient solutions to combat urban flash floods, as well as contribute to targeted flood damage mitigation and flood risk reduction.

Climate adaptive urban measures in Mediterranean areas: Thermal effectiveness of an advanced multilayer green roof installed in Palermo (Italy)

Several nature based and climate adaptive solutions have been proposed to improve cities resilience to the effects of global warming and restore natural processes in strongly anthropized areas. Green roofs are among the most efficient nature based solutions to address recurrent urban challenges, such as pluvial floods and urban heat islands. Various benefits offered by green roofs are rather known, such as their capacity to enhance buildings thermal insulation; green roofs also favor urban biodiversity, improving buildings aesthetic value and human well being. Multilayer green roofs (MGRs) are green roofs with an additional layer that increases their water storage capacity. Deep analyses on MGRs are still lacking due to their recent development, and the few works in literature are prevalently focused on their stormwater retention primary function. This work explores the thermal function of an experimental MGR prototype installed in Palermo (Italy), comparing its response to local climate with that of an unaltered portion of the rooftop through the analysis of surface temperature time series collected over a two years monitoring period. Performances are evaluated thought various daily thermal indices, also analyzing the role of the water stored into the system. Results contribute to raise awareness about the benefits arising from the use of MGRs in semi-arid Mediterranean urban areas, confirming, as main thermal advantage, their cooling effect, with mean daily surface temperature reduced by 8.4% outdoor and 5.8% indoor; performances increases with water storage and are particularly evident during the hot and dry summers that typically characterize such regions.

Challenges for Flood Risk Reduction in Poland’s Changing Climate

Floods are the main natural disaster in Poland, and the risk of both fluvial and pluvial floods is serious in the country. Pluvial floods are on the rise in the changing climate, particularly in increasingly sealed urbanized areas. In this paper, we examine the changes in flood risk in Poland, discussing the mechanisms, observations, projections and variability. Next, we discuss flood risk management in the country, including specific issues related to urban and rural areas and the synergies between flood and drought risk reduction measures. We identify and assess the weaknesses of the existing flood risk management plans in Poland for the first planning period 2016–2021 and for the second planning period 2022–2027. We find the level of implementation of plans in the former period to be very low. Many planned measures do not have much to do with flood risk reduction but are often linked to other objectives, such as inland navigation. The plans contain numerous small measures, which come across as inapt and economically ineffective solutions. We specify policy-relevant recommendations for necessary and urgent actions, which, if undertaken, could considerably reduce flood risk. We also sketch the way ahead for flood risk management in Poland within the timeframe of the implementation of plans for 2022–2027 and the next regular update of plans for 2028–2033.

Pluvial flood adaptation using nature-based solutions: An integrated biophysical-economic assessment

Globally, flood events are considered the costliest natural hazard. Changes in precipitation patterns and large areas of impervious surfaces in urban environments are increasing the sensitivity of these systems to runoff production. At the same time, projected global sea-level rise may further increase the frequency of compound flooding due to simultaneous storm surge, sea-level rise and pluvial runoff that cause vast socio-economic and ecological impacts to coastal cities. In this context, over the last decade, the role of Nature-Based Solutions (NBS) has been recognised to support climate change adaptation by addressing ideas of multi-functionality, non-linearity and heterogeneity in urban design. Thus, increasing awareness about NBS benefits increases the willingness to accept these solutions. However, empirical evidence of NBS effectiveness at the urban catchment scale is still subject to debate. This study develops a spatial biophysical-economic framework that allows for the integrated assessment of NBS flood risk mitigation impacts, costs and benefits in the face of climate change, combining the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, benefit transfer methods and Geographic Information System (GIS) tools. Specifically, the InVEST Urban Flood Risk Mitigation model was used to assess the biophysical impacts of NBS on urban pluvial flood risk, benefit-transfer methods were used to evaluate the economic implications of such solutions, and GIS was used to integrate and map biophysical impacts and economic implications. For the case of the coastal lagoon city of Aveiro (Portugal), NBS scenarios of green roofs and bioswales under current and future climate conditions were assessed. The main findings of this study show that green roofs scenarios would save 32 % of the flood damages to buildings and infrastructures every year, while bioswales help save only 0.1 %. Moreover, green roofs implementation provides larger benefits in the future climate scenario (representative concentration pathway – RCP – 4.5). The findings confirm the extent to which knowledge on NBS benefits and costs is partial and uncertain, thus requiring constant progress through biophysical-economic assessment to support an evolutive decision making process in climate adaptation planning.

From believing in climate change to adapting to climate change: The role of risk perception and efficacy beliefs.

Are people more inclined to adapt to climate change if they believe that climate change is real, caused by human behavior, and/or brings negative consequences? Previous studies provided inconclusive results on the relationship between climate change perceptions and adaptation behavior. Using a longitudinal approach, we examined whether risk perception, self-efficacy, and outcome efficacy play a role in how and when climate change perceptions are associated with adaptation to pluvial flooding and heat stress in the Netherlands. As expected, stronger climate change perceptions were associated with stronger perceptions of climate-related risks, which in turn lead to stronger intentions to implement adaptation measures. Yet, neither climate change perceptions nor risk perception were associated with whether people had actually implemented adaptation measures during a 1-year period. Contrary to common assumptions in the literature, higher levels of perceived self-efficacy and outcome efficacy did not strengthen the relations between climate change perceptions and adaptation intentions and behavior. Rather, higher levels of self-efficacy and outcome efficacy were directly related to stronger intentions to take adaptive measures and taking those measures within a period of 1 year. Exploratory analyses offered initial support for a sequential model where climate change perceptions lead to higher levels of perceptions of specific climate-related risks, which in turn lead to stronger self-efficacy and outcome efficacy, ultimately increasing adaptation intentions, but not actual behavior. Strategies to promote adaptation behavior could aim to remove behavioral barriers to increase self-efficacy, and strengthen outcome efficacy, enabling people to act on their climate change perceptions.

Fast simulation and prediction of urban pluvial floods using a deep convolutional neural network model

Urban pluvial floods induced by rainstorms can cause severe losses to human lives and property. Fast and accurate simulation and prediction of urban pluvial flood are of significance for disaster prevention and mitigation. However, physics-based models still experience excessive computational time when used for flood simulation and prediction. In this study, we explore a deep learning (DL) approach employing convolutional neural networks (CNN) as an alternative model to achieve fast prediction of urban floods. We first cluster a rainstorm-inundation database generated using a physics-based coupled model, then we develop a CNN model for predicting the spatial and temporal evolution of rainstorm inundation, and finally we compare the effectiveness of the CNN model with that of the coupled model and three other classical machine learning (ML) models. The results show that: 1) The inundation water depths predicted using the CNN model are close to those predicted using the coupled model, and the average PCC, MAE and RMSE metrics under a test rainstorm reach 0.983, 0.020 m and 0.086 m, respectively. 2) The CNN model reproduces well the trend of water depth in each model grid cell over time, especially for a heavily inundated grid. 3) The predicted effectiveness of the CNN model outperforms an extreme gradient boosting (XGBoost) model, followed by a multi-objective random forest (MORF) model and K-nearest neighbor (KNN) model. 4) The computational speed of the CNN model is extremely fast. The model can simulate inundation water depth with a spatial resolution of 8 m by 8 m (about 74 km2) and a temporal resolution of 30 min for a 6-hour lead time within 12 s, which is 600 times faster than that of a coupled model. We confirm that the CNN model with clustering method is a powerful surrogate model for fast simulation of urban pluvial flood, providing an important reference for the use of DL in early warning and mitigation in relation to urban flood disasters.

Coping Capacity, Adaptive Capacity, and Transformative Capacity Preliminary Characterization in a “Multi-Hazard” Resilience Perspective: The Soccavo District Case Study (City of Naples, Italy)

An innovative methodology for characterizing Coping Capacity (CC), Adaptive Capacity (AC), and Transformative Capacity (TC) resilience determinants in a multi-hazard territorial context is applied to the Soccavo district (Naples, Italy), located in the Campi Flegrei caldera and exposed to volcanic eruptions, ground deformations (bradyseism), earthquakes, geomorphological processes (landslides, rock falls, erosion), and climate change-related hazards (heat waves, pluvial floods). The method allowed for the identification of the CC, AC, and TC parameters that can be easily converted into qualitative–quantitative variables. Among all parameters, the method focused on multi-stakeholder and civil society engagement, which is representative of TC and variable relating to the risk perception and awareness, the urban space perception, or the people’s ability to activate bottom-up urban transformation processes within resilient development pathways. Therefore, qualitative tools such as collaborative mapping and co-design processes, pertaining to the urban planning and design fields, and quantitative tools such as surveys, from the social science field, were harmonized and combined to collect and analyze data on these site-specific topics. Considering people’s priorities and needs, the study was useful to define shared sustainable and resilient solutions in order to holistically integrate Disaster Risk Reduction/Climate Change Adaptation urban planning and design approaches and simultaneously deliver social, environmental, and economic co-benefits.

A Study on the Accessibility of the Emergency Medical Services for Urban Kindergartens and Nursing Homes Based on Urban Pluvial Flooding Scenarios

Vulnerable groups such as children and the elderly are the focus of emergency medical rescue during urban pluvial floods. Taking the Erqi District of Zhengzhou City as an example, the SCS-CN model and Chicago rainfall model are used to simulate pluvial flooding based on the comprehensive consideration of urban rainfall, runoff, topography, and drainage. Additionally, the accessibility of emergency medical aid for kindergartens and nursing homes is evaluated in the Erqi District of Zhengzhou under different pluvial flooding scenarios using GIS network analysis technology. The results showed that the number of kindergartens and nursing homes without timely access to emergency medical rescue increased with the increase in precipitation return periods. Under the 500-year and 1000-year pluvial flooding scenarios, kindergartens and nursing homes that can obtain emergency medical rescue had delayed response times. Furthermore, with the increase in the precipitation return periods, both the number and delay time of kindergartens and nursing homes significantly increased. The accessibility and delay time of emergency medical rescue in kindergartens and nursing homes were determined by the intensity of pluvial flooding (including inundation area and depth), road traffic conditions, and the number and location of medical institutions, nursing homes, and kindergartens. The research results can provide a scientific basis for improving the refinement level of urban flood disaster management and emergency response services.

Adaptation strategy with public space for pluvial flood risk mitigation in a densely populated city: A case study in Huwei, Taiwan

Study regionHuwei Township is located in Yunlin County, Taiwan, and its urban planning district covers an area of 5.29 km2. The land development in the district is close to saturation, and most of the public facilities have been developed. The flat terrain and the uncompleted storm sewer system lead to frequent flooding. Study focusThis study presents an adaptation strategy of adopting public space to build flood retention facilities (FRF) for pluvial flood risk mitigation in a densely populated city. This study integrates risk and benefit-cost analysis to evaluate the proposed strategy's flood mitigation effect and economic benefit. Huwei is taken as a study region to compare this strategy with traditional storm sewer (SS) construction. New hydrological insightsSS construction reduces flooded areas more than building FRF, leading to more significant flood damage. Since public space is adjacent to residential areas, FRF can directly and effectively reduce residence-related flood damage. Additionally, building FRF is less expensive than building SS. The benefit-cost ratio of building FRF is 0.45, more than twice the 0.21 of building SS. Therefore, we recommend prioritizing the construction of FRF in Huwei rather than finishing the construction of SS. This adaptation strategy can be applied to densely populated cities facing the same problems.

Urban climate resilience under racial capitalism: Governing pluvial flooding across Amsterdam and Dhaka

This paper situates climate resilience as a solution to urban flooding under racial capitalism by examining its implementation across Amsterdam, the Netherlands, and Dhaka, Bangladesh. The necessity for cities to enhance their climate resilience in the face of urban flooding has become a dominant refrain in the global governance of climate change. This is the case in both the Netherlands and Bangladesh, locations that are respectively and divergently framed as global “masters” of water management and needy “apprentices” requiring international guidance on flood control and economic development. I place these two sites in conversation with one another to break down constructed ontological and epistemological divides across the global North and global South, drawing attention to the relationship between contemporary urban climate resilience policies and broader histories of socio-ecological injustice under racial capitalism. I argue that urban climate resilience policies across Amsterdam and Dhaka renegotiate and reconfigure techniques, practices and ideologies of racial and class-based inequality in response to threats and insecurity posed by climate change within and across urban scales. Looking across Amsterdam and Dhaka, I demonstrate that climate resilience policies primarily aim to protect status quo forms of capital accumulation at the urban scale rather than enact comprehensive climate adaptation measures. As a result, the ineffective and inequitable nature of resilience continues to support highly racially uneven urban socio-natures across the global North and global South, enabled by racist imaginaries where communities in the global South – particularly the urban poor – are framed as being undeserving of environmental justice.

Awareness and willingness to pay for green roofs in Mediterranean areas

Green roofs have been extensively investigated in recent years, showing that their implementation in urban areas provides multiple benefits (e.g., pluvial flood mitigation, urban heat island reduction, energy saving, increase of biodiversity, CO2 sequestration) and supports sustainable urban development. Although green roof benefits have been widely recognized, the perception that the community has of these nature-based solutions and the willingness to pay for their installation in urban areas is still not clear nor quantified. Societal perception and willingness to pay for green roofs are fundamental for urban planners and decision makers, since they represent the community participation in the sustainable development of urban areas. In this work, we aim to analyze how citizens perceive green roofs and how willing they are to pay for the installation and maintenance of these nature-based solutions. We used an online survey to investigate the perception and the knowledge of green roofs as a potential solution to common environmental issues (i.e., urban flood, increase of temperature, energy consumption, air pollution and lack of green spaces), and the interest and willingness to pay for green roof installation on both public and private roofs. Based on the answers of 389 respondents living in Sardinia (Italy), our analysis revealed that most citizens are aware of what green roofs are, and they are aware that, although these nature-based solutions can not completely solve environmental issues, they can greatly contribute to the mitigation of these phenomena. Results also show a higher interest in the installation of green roofs on public buildings than on private ones, due to the high installation costs. Moreover, for private roofs, the possibility to install photovoltaic panels instead of GRs is generally preferred. Most of the respondents are willing to spend less than 100 € per year for the maintenance of green roofs on public buildings and to invest less than 5000 € for the installation on their own house.

Assessing present and future risk of water damage using building attributes, meteorology, and topography*

Abstract Weather-related risk makes the insurance industry inevitably concerned with climate and climate change. Buildings hit by pluvial flooding is a key manifestation of this risk, giving rise to compensations for the induced physical damages and business interruptions. In this work, we establish a nationwide, building-specific risk score for water damage associated with pluvial flooding in Norway. We fit a generalised additive model that relates the number of water damages to a wide range of explanatory variables that can be categorised into building attributes, climatological variables, and topographical characteristics. The model assigns a risk score to every location in Norway, based on local topography and climate, which is not only useful for insurance companies but also for city planning. Combining our model with an ensemble of climate projections allows us to project the (spatially varying) impacts of climate change on the risk of pluvial flooding towards the middle and end of the 21st century.

Modeling compound flood risk and risk reduction using a globally applicable framework: a pilot in the Sofala province of Mozambique

Abstract. In low-lying coastal areas floods occur from (combinations of) fluvial, pluvial, and coastal drivers. If these flood drivers are statistically dependent, their joint probability might be misrepresented if dependence is not accounted for. However, few studies have examined flood risk and risk reduction measures while accounting for so-called compound flooding. We present a globally applicable framework for compound flood risk assessments using combined hydrodynamic, impact, and statistical modeling and apply it to a case study in the Sofala province of Mozambique. The framework broadly consists of three steps. First, a large stochastic event set is derived from reanalysis data, taking into account co-occurrence of and dependence between all annual maximum flood drivers. Then, both flood hazard and impact are simulated for different combinations of drivers at non-flood and flood conditions. Finally, the impact of each stochastic event is interpolated from the simulated events to derive a complete flood risk profile. Our case study results show that from all drivers, coastal flooding causes the largest risk in the region despite a more widespread fluvial and pluvial flood hazard. Events with return periods longer than 25 years are more damaging when considering the observed statistical dependence compared to independence, e.g., 12 % for the 100-year return period. However, the total compound flood risk in terms of expected annual damage is only 0.55 % larger. This is explained by the fact that for frequent events, which contribute most to the risk, limited physical interaction between flood drivers is simulated. We also assess the effectiveness of three measures in terms of risk reduction. For our case, zoning based on the 2-year return period flood plain is as effective as levees with a 10-year return period protection level, while dry proofing up to 1 m does not reach the same effectiveness. As the framework is based on global datasets and is largely automated, it can easily be repeated for other regions for first-order assessments of compound flood risk. While the quality of the assessment will depend on the accuracy of the global models and data, it can readily include higher-quality (local) datasets where available to further improve the assessment.