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Integrated risk management requires all stakeholders to work together proactively. Residents of floodplains can participate by implementing property-level adaptive measures. Risk communication materials can motivate those households to do so. Research on these materials is limited. Therefore, we systematically assessed freely available German risk communication materials in terms of their recommendations and how their content aligns with behavioural theories. We compare these results with data from surveyed households affected by urban flooding (N = 1,352) on their attitudes towards flood adaptation and the adaptation measures implemented. 209 risk communication materials were reviewed. Adaptation options (mostly evasion or resistance strategies) were communicated in 93%, the hazard itself in 78%, the local hazard situation in 48%, and responsibilities in risk management in 54% of the risk communication materials. These aspects were rarely broken down for the reader by, for example, presenting the damage that can be expected or by presenting measures with the expected costs or their response efficacy. However, these details commonly increase the adaptive behaviour of residents according to commonly used behavioural theories. Survey data indicated that households feel able (82%) and responsible (41%) for implementing adaptive measures. However, many households indicated that measures are not effective (41%), too expensive (34%), and that there are not enough support programs (87%). We recommend that future risk communication materials focus more on communicating the efficacy and costs of adaptive measures and highlight funding opportunities. Our mixed method approach highlighted mismatches in information needed and provided. [ABSTRACT FROM AUTHOR]

Pluvial flooding is a critical issue in cities worldwide, particularly in lowland areas with old and deteriorating drainage systems. The primary driver of pluvial flooding is extreme rainfall; other drivers include urbanization, inadequate drainage systems, improper solid-waste management, and the tidal backwater effect. However, the interplay between these drivers makes predicting pluvial floods difficult and complex. Previous studies in developing countries seldom used water-level data or simulation modeling to identify the causes of pluvial flooding. In this study, rainfall data and water-level variations in an open channel drain and a receiving river controlled by sluice gates were collected and evaluated in detail to investigate pluvial flooding events. To predict these events, we generated a hydrodynamic model using InfoWorks ICM and verified its results using water logger data and official field reports. Analysis shows that drainage-system failures due to solid blockage and receiving water-level variation contribute more to pluvial flood occurrence than heavy rainfall. Lastly, we discuss measures to mitigate pluvial flooding in Yangon, Myanmar. The proposed monitoring and modeling approach can suitably predict pluvial flooding occurrence and provide useful quantitative data for flood risk management. [ABSTRACT FROM AUTHOR]

Climate-related risks, vulnerabilities, and impacts are increasing in cities, illustrated by precipitation-driven pluvial floods. Post-event analyses can aid in reducing urban flood risks, but knowledge gaps exist regarding how welfare services and premises are impacted and can be adapted. This study analyses an extreme precipitation-driven event generating extensive flooding in Gävle, Sweden, in 2021. The objective is to increase knowledge about how municipal welfare services are vulnerable to pluvial floods, and of appropriate actions towards improving the response capacity and building more resilient welfare premises and operations. The study shows that the Swedish weather warning system generally worked well, but the analysed property companies lacked strategies and equipment to evade flooding in their properties. Flood damages in 60 analysed buildings were generated by different causes, demonstrating the importance of contemplating the vulnerability of welfare buildings when conducting flood risk assessments. Although the flood event did not cause deaths or serious personal injuries, the study identified impacts on welfare service operations in both the short and long terms. The event increased learning on climate adaptation but did not trigger adaptive action. Identified keys for adaptation include prioritizing premises to protect, knowledge of flood protection equipment, insurance company requirements, and updated emergency plans. HIGHLIGHTS Results show that the Swedish weather warning system generally worked well.; Short- and long-term impacts on welfare buildings and services are reported.; The 60 damaged buildings analysed were found flooded due to different causes.; The amount of damage was driven by the vulnerability of buildings rather than exposure.; The pluvial flood increased learning on adaptation but did not trigger adaptive action.;

Robust and accurate flood hazard maps are essential for early warning systems and flood risk management. Although physically based models are effective in estimating pluvial flooding, the computational burden makes them difficult to use for real-time flood prediction. In contrast, data-driven models can provide faster flood predictions if trained offline. While most studies have focused on predicting maximum water depth, in this study, we predict pixel-wise water depth maps for entire catchments at a lead time of 2 h. To that end, we propose a deep learning approach that uses a sequence encoding network with temporal self-attention. We also adapt the popular hydrological performance metric Nash–Sutcliffe efficiency (NSE) as our loss function. We test the effectiveness and generalizability of our method using a new dataset called SwissFlood, which consists of 100 catchments and 1500 rainfall events extracted from real observations in Switzerland. Our method produces 2 m spatial resolution flood maps with absolute error as low as 27 cm for water depth exceeding 1 m. [ABSTRACT FROM AUTHOR]

Coastal urban areas like New York City (NYC) are more vulnerable to urban pluvial flooding particularly because the rapid runoff from extreme rainfall events can be further compounded by the co-occurrence of high sea-level conditions either from tide or storm surge leading to compound flooding events. Present-day urban pluvial flooding is a significant challenge for NYC and this challenge is expected to become more severe with the greater frequency and intensity of storms and sea-level rise (SLR) in the future. In this study, we advance NYC's assessment of present and future exposure to urban pluvial flooding through simulating various storm scenarios using a citywide hydrologic and hydraulic model. This is the first citywide analysis using NYC's drainage models focusing on rainfall-induced flooding. We showed that the city's stormwater system is highly vulnerable to high-intensity short-duration "cloudburst" events, with the extent and volume of flooding being the largest during these events. We further showed that rainfall events coupled with higher sea-level conditions, either from SLR or storm surge, could significantly increase the volume and extent of flooding in the city. We also assessed flood exposure in terms of the number of buildings and length of roads exposed to flooding as well as the number of the affected population. This study informs NYC's residents of their current and future flood risk and enables the development of tailored solutions to manage increasing flood risk in the city. [ABSTRACT FROM AUTHOR]

Climate-related risks, vulnerabilities, and impacts are increasing in cities, illustrated by precipitation-driven pluvial floods. Post-event analyses can aid in reducing urban flood risks, but knowledge gaps exist regarding how welfare services and premises are impacted and can be adapted. This study analyses an extreme precipitation-driven event generating extensive flooding in Gävle, Sweden, in 2021. The objective is to increase knowledge about how municipal welfare services are vulnerable to pluvial floods, and of appropriate actions towards improving the response capacity and building more resilient welfare premises and operations. The study shows that the Swedish weather warning system generally worked well, but the analysed property companies lacked strategies and equipment to evade flooding in their properties. Flood damages in 60 analysed buildings were generated by different causes, demonstrating the importance of contemplating the vulnerability of welfare buildings when conducting flood risk assessments. Although the flood event did not cause deaths or serious personal injuries, the study identified impacts on welfare service operations in both the short and long terms. The event increased learning on climate adaptation but did not trigger adaptive action. Identified keys for adaptation include prioritizing premises to protect, knowledge of flood protection equipment, insurance company requirements, and updated emergency plans. [ABSTRACT FROM AUTHOR]

One of the issues of greatest interest in urban planning today concerns the evaluation of the most vulnerable urban areas in the presence of different types of climate hazards. In this research, a hierarchical fuzzy MCDA model is implemented on a GIS-based platform aimed at detecting the urban areas most at risk in the presence of heatwave and pluvial flooding scenarios. The proposed model aims to detect the urban areas most vulnerable to both the two climatic phenomena and the two types of hazards as independent events; it partitions the physical component of an urban settlement into two subsystems: buildings and open spaces, and it determines the criticality of a subzone of the urban area of study by evaluating the vulnerabilities of the two subsystems to the two phenomena. The use of a hierarchical fuzzy MCDA model facilitates the modeling of the two subsystems and the assessment of their vulnerability to the two phenomena, and it provides a computationally fast tool for detecting critical urban areas. The model was tested on a study area made up of the districts of the central-eastern area of the city of Naples (Italy); it was divided into subzones made up of individual census areas. The most critical areas are represented by the subzones with criticality values higher than a specific threshold. [ABSTRACT FROM AUTHOR]

Flood risk in cities and built‐up areas is a major threat which is likely to grow due to increased urbanisation and climate change. It is a priority for urban planning, civil defence and insurance to accurately represent buildings and urban features in hydrodynamic models to assess flood risk to people, properties, assets and infrastructure in an uncertain future. The correct representation of urban features in models is currently blocked by the lack of detailed and accurate techniques and has become a priority for the improvement of urban flood modelling now that better data and computational resources are available. This study has reviewed the available approaches for the representation of buildings and urban features and implemented the widely used 'stubby building' approximation as well as a more realistic and innovative 'building hole' approach using the hydrodynamic model CityCAT. The city centre of Newcastle upon Tyne, UK, was used as a case study, allowing independent validation of the methods and direct, systematic comparison of performance. Shortcomings of the approximate method are described, and guidance given on limits to its reliable application and scope for improvement. [ABSTRACT FROM AUTHOR]

There is an urgent need for urban environments to be flood resilient due to increasing urbanization and climate change. This can be addressed by adopting sustainable drainage solutions (SuDS) in households. However, lack of knowledge and awareness among urban residents is a barrier. In this paper, we present an educational serious game called SuDSbury to overcome this barrier and a pre-/post-game survey-based evaluation to study whether the game can educate citizens (and to what degree). An exploratory study with 14 players across three game sessions suggests that playing SuDSbury induced changes in knowledge, comprehension, and personal norms regarding SuDS. However, comprehension of concepts related to urban drainage can be improved by increasing game realism. The game should be further tested with a larger sample and a diverse demographic of urban residents. The participants further found that SuDSbury is fun and engaging to play, making it suitable for broader public interventions. [ABSTRACT FROM AUTHOR]

Hurricane Harvey (2017) resulted in unprecedented damage from flooding in the Houston–Galveston area of the U.S. Gulf Coast. The objective of this study was to better quantify the impacts of compound flooding and to assess the relative contributions of storm surge, pluvial (rainfall) and fluvial (riverine) flooding using Hurricane Harvey as a case study. Here we developed a comprehensive numerical modeling framework to simulate flood extents and levels during Hurricane Harvey using Delft3D Flexible Mesh and validated results against observed water levels, waves, winds, hydrographs and high water marks. Results show that pluvial flooding dominated from widespread heavy rainfall during Harvey, accounting for ~ 60–65% of flooding. Pluvial flooding occurred mostly in watersheds and floodplains in West and South Bays (≤ ~ 1.5 m), upper Galveston Bay (Trinity River Basin, 2–3 m) and Harris County (≤ ~ 2.5 m). River runoff led to local ~ 1–2 m flooding. Significant storm surge levels were simulated northwest of the main Bay (2–2.5 m) and Galveston Bay (1–2 m) and in several watersheds in West/East of Galveston Bay. Wave action caused flood depth and water levels to rise by about 0.3–0.5 m in nearshore areas. Maximum flooding extent developed around August 29, 2017, which compared well to FEMA flood depth data. Nonlinear effects of compound flooding are greater than the sum of individual components. Results from this large-scale coupled modeling analysis provide a useful basis for coastal risk management and hazard mitigation. Our integrated framework is general and can be readily applied to other coastal compound flooding analyses. [ABSTRACT FROM AUTHOR]

Polders are low-lying areas located in deltas, surrounded by embankments to prevent flooding (river or tidal floods). They rely on pumping systems to remove water from the inner rivers (artificial rivers inside the polder area) to the outer rivers, especially during storms. Urbanized polders are especially vulnerable to pluvial flooding if the drainage, storage, and pumping capacity of the polder is inadequate. In this paper, a Monte Carlo (MC) framework is proposed to evaluate the benefits of rainfall threshold-based flood warnings when mitigating pluvial flooding in an urban flood-prone polder area based on 24 h forecasts. The framework computes metrics that give the potential waterlogging duration, maximum inundated area, and pump operation costs by considering the full range of potential storms. The benefits of flood warnings are evaluated by comparing the values of these metrics across different scenarios: the no-warning, perfect, deterministic, and probabilistic forecast scenarios. Probabilistic forecasts are represented using the concept of "predictive uncertainty" (PU). A polder area located in Nanjing was chosen for the case study. The results show a trade-off between the metrics that represent the waterlogging and the pumping costs, and that probabilistic forecasts of rainfall can considerably enhance these metrics. The results can be used to design a rainfall threshold-based flood early warning system (FEWS) for a polder area and/or evaluate its benefits. [ABSTRACT FROM AUTHOR]

Abstract Flood risk in cities and built‐up areas is a major threat which is likely to grow due to increased urbanisation and climate change. It is a priority for urban planning, civil defence and insurance to accurately represent buildings and urban features in hydrodynamic models to assess flood risk to people, properties, assets and infrastructure in an uncertain future. The correct representation of urban features in models is currently blocked by the lack of detailed and accurate techniques and has become a priority for the improvement of urban flood modelling now that better data and computational resources are available. This study has reviewed the available approaches for the representation of buildings and urban features and implemented the widely used ‘stubby building’ approximation as well as a more realistic and innovative ‘building hole’ approach using the hydrodynamic model CityCAT. The city centre of Newcastle upon Tyne, UK, was used as a case study, allowing independent validation of the methods and direct, systematic comparison of performance. Shortcomings of the approximate method are described, and guidance given on limits to its reliable application and scope for improvement.

Ancient monuments located in urbanized areas are subject to numerous short- and long-term environmental hazards with flooding being among the most critical ones. Flood hazards in the complex urban environment are subject to large spatial and temporal variability and, thus, require location-specific risk assessment and mitigation. We devise a methodological scheme for assessing flood hazard in urban areas, at the monument’s scale, by directly routing rainfall events over a fine-resolution digital terrain model at the region of interest. This is achieved using an open-source 2D hydraulic modelling software under unsteady flow conditions, employing a scheme known as ‘direct rainfall modelling’ or ‘rain-on-grid’. The method allows for the realistic representation of buildings and, thus, is appropriate for detailed storm-induced (pluvial) flood modelling in urbanized regions, within which a major stream is usually not present and conventional hydrological methodologies do not apply. As a case study, we perform a pilot assessment of the flood hazard in the Roman Agora, a major archaeological site of Greece located in the center of Athens. The scheme is incorporated within an intelligent decision-support system for the protection of monumental structures (ARCHYTAS), allowing for a fast and informative assessment of the flood risk within the monument’s region for different scenarios that account for rainfall’s return period and duration as well as uncertainty in antecedent wetness conditions.

A static flood analysis (SFA) toolset is implemented with the purpose of performing simplified, event-based flood inundation modeling. The simulation process is divided into two steps: a topographic sink analysis of the terrain followed by flow accumulation (FA) of runoff volumes. Both procedures are coupled to account for sink storage effects. The sink analysis procedure determines the morphology of present sinks that are utilized during FA to calculate the sink storage by solving a mass balance, whereby inflows are captured in each sink according to their capacity and overflows are routed further downstream. Each sink is enriched with a set of hydrological attributes such as the total inflow, overflow, and flood depth. The flood depth is then utilized to determine the flood extent and flood depths of each sink. Two main options are made available for the FA procedure: either a single flow direction (SFD) or a novel formulation of the multiple flow direction (MFD) algorithm. Both methods were compared in terms of their accuracy with results from the TELEMAC-2D finite-volume solver for an urban inundation model. Both methods showed good agreement when compared with the validation results, with the MFD method performing marginally better than the SFD method. HIGHLIGHTS A method for rapid generation of pluvial flood maps is developed using topographic sink analysis and FA methods.; A variety of outputs are generated, such as flood extents, flood depths, FA volumes, and topographic sink attributes.; An SFD and a novel MFD formulation is developed and used.; The proposed methods showed a good agreement when compared to a hydrodynamic model and clearly outperformed it in terms of computational effort.;

Jamaica has had over 200 floods in the past 50 years, causing significant human and economic losses. Kingston has often caused the most damage due to its high population density and capital exposure. Kingston is crucial to the country's socio-economic stability, and climate change is increasing flood risk, but a local-scale assessment of its flood risk is unavailable. This study fills this gap in the literature by using two models of the integrated Valuation of Ecosystem Services and Tradeoffs (inVEST) suite to qualitatively assess Kingston metropolitan pluvial and coastal flood risk. Key locations like Kingston Container Terminal and downtown Kingston are at high coastal flood risk, according to the results. The study also shows that sea level rise (117%) and habitat loss (104%) will increase the highly exposed area. Instead of hard-engineering coastal protection, this study suggests investing in nature-based and ecoengineering solutions to improve coastal resilience and ecosystem services. The urban flood assessment finds downtown, particularly the Mountview and Minor catchments, at high risk due to poor runoff retention and high population density. To fully address downtown pluvial flood risk, structural social reforms are needed. To reduce short-term flood risk, local authorities should consider targeted adaptation measures. These may include maintaining the drainage gully system and reducing surface runoff in uphill downtown areas. Thus, this study seeks to inform Kingston urban planners about risk distribution and suggest adaptation measures to improve flood resilience. [ABSTRACT FROM AUTHOR]

Ancient monuments located in urbanized areas are subject to numerous short- and long-term environmental hazards with flooding being among the most critical ones. Flood hazards in the complex urban environment are subject to large spatial and temporal variability and, thus, require location-specific risk assessment and mitigation. We devise a methodological scheme for assessing flood hazard in urban areas, at the monument's scale, by directly routing rainfall events over a fine-resolution digital terrain model at the region of interest. This is achieved using an open-source 2D hydraulic modelling software under unsteady flow conditions, employing a scheme known as 'direct rainfall modelling' or 'rain-on-grid'. The method allows for the realistic representation of buildings and, thus, is appropriate for detailed storm-induced (pluvial) flood modelling in urbanized regions, within which a major stream is usually not present and conventional hydrological methodologies do not apply. As a case study, we perform a pilot assessment of the flood hazard in the Roman Agora, a major archaeological site of Greece located in the center of Athens. The scheme is incorporated within an intelligent decision-support system for the protection of monumental structures (ARCHYTAS), allowing for a fast and informative assessment of the flood risk within the monument's region for different scenarios that account for rainfall's return period and duration as well as uncertainty in antecedent wetness conditions. [ABSTRACT FROM AUTHOR]

The 400-year history of Dhaka says that the city once had several well-known natural canals (khals) that drained stormwater and graywater. In addition to city's combined sewer system, these water bodies offered an essential natural drainage system that allowed to manage the monsoon rainfall effectively. However, over the past three decades, due to rapid urbanization, these khals have significantly depleted to the point where they are no longer capable of draining the city's monsoon runoff. Using past, present, and future Land Use and Land Cover (LULC) and urban drainage modeling, this study identified the effects of such LULC change on pluvial flooding of the northern part of the city. Analysis shows that the rapid and extensive changes in LULC over the past decades have resulted in significant shrinkage of these khals, consequently leading to escalated rates of urban flooding in this region. The western part of Turag thana, low-lying areas close to the Baunia Khal depression, and the upstream region of Abdullahpur Khal are highly vulnerable to future urban floods. The projected LULC change indicates an increase of 8.47%, 8.11%, and 4.05% in the total inundation area by 2042 for rainfall events with return periods of 50 years, 25 years, and 2.33 years, respectively. The findings also indicate that 11% more area is likely to experience long-duration flooding due to LULC change. [ABSTRACT FROM AUTHOR]

Devastating floods have plagued many West African cities in the past decades. In an attempt to reduce flood damage in Bamako (Mali), an early warning system (EWS) demonstrator (Raincell App) was developed for flash floods. On 16 May 2019, while the demonstrator was partially operational, an intense rainfall event led to devastating floods. We carried out an experience feedback on this flood event by comparing EWS simulations to the results of a field survey. Given the synoptic situation and the rapid development pattern of the storm, none of the global forecasting systems were able to foresee its occurrence and magnitude. The hydrological model developed as part of the demonstrator correctly identified most of the locations where overbank flow occurred. In the absence of data, the predicted discharge and volume values could not be validated. However, they are realistic based on the water levels reported in the Post‐Disaster Needs Assessment report. It would be advisable to couple it to a two‐dimensional hydraulic model and add discharge and water level monitoring to the already existing rainfall surveillance scheme to further improve the system's performance. Increasing the local population's awareness of the dangers of clogged waterways is also mandatory. [ABSTRACT FROM AUTHOR]

This paper demonstrates the potential for crowdsourced rainfall data to infill gaps in the official rain gauge network and to provide new datasets for use in research. We use data from the Met Office Weather Observation Website (WOW) over 10 years (2011–2020) to generate two open-source datasets for Britain; multi-parameter raw data in an easy-to-use format; and an hourly rainfall dataset. We have compiled and prepared the data and detail here station selection, rain depth calculation, and data resampling to hourly intervals to create a consistent dataset for further processing (including statistical quality control) and application. Mapping the new rainfall dataset establishes that WOW observations fill spatial gaps in the official ground-based rain gauge network over Britain, particularly in urban areas. This could be particularly useful for post-event analysis of rainfall that results in pluvial flash flooding. Here, we focus on Britain but due to agreements with meteorological services in Belgium, the Netherlands, Australia, New Zealand, Sweden, and the Republic of Ireland, plus many citizen scientists globally opting to share data via WOW, there is potential for the development of similar datasets using these methods around the world. HIGHLIGHTS Processing of British citizen science crowdsourced data from the Met Office WOW database from 2011 to the end of 2020.; Distribution of rainfall data from ground-based rain gauges in Britain.; The potential of citizen science weather data.; Dataset access.;

Robust and accurate flood hazard maps are essential for early warning systems and flood risk management. Although physically based models are effective in estimating pluvial flooding, the computational burden makes them difficult to use for real-time flood prediction. In contrast, data-driven models can provide faster flood predictions if trained offline. While most studies have focused on predicting maximum water depth, in this study, we predict pixel-wise water depth maps for entire catchments at a lead time of 2 h. To that end, we propose a deep learning approach that uses a sequence encoding network with temporal self-attention. We also adapt the popular hydrological performance metric Nash–Sutcliffe efficiency (NSE) as our loss function. We test the effectiveness and generalizability of our method using a new dataset called SwissFlood, which consists of 100 catchments and 1500 rainfall events extracted from real observations in Switzerland. Our method produces 2 m spatial resolution flood maps with absolute error as low as 27 cm for water depth exceeding 1 m.

One of the issues of greatest interest in urban planning today concerns the evaluation of the most vulnerable urban areas in the presence of different types of climate hazards. In this research, a hierarchical fuzzy MCDA model is implemented on a GIS-based platform aimed at detecting the urban areas most at risk in the presence of heatwave and pluvial flooding scenarios. The proposed model aims to detect the urban areas most vulnerable to both the two climatic phenomena and the two types of hazards as independent events; it partitions the physical component of an urban settlement into two subsystems: buildings and open spaces, and it determines the criticality of a subzone of the urban area of study by evaluating the vulnerabilities of the two subsystems to the two phenomena. The use of a hierarchical fuzzy MCDA model facilitates the modeling of the two subsystems and the assessment of their vulnerability to the two phenomena, and it provides a computationally fast tool for detecting critical urban areas. The model was tested on a study area made up of the districts of the central-eastern area of the city of Naples (Italy); it was divided into subzones made up of individual census areas. The most critical areas are represented by the subzones with criticality values higher than a specific threshold.

Abstract Devastating floods have plagued many West African cities in the past decades. In an attempt to reduce flood damage in Bamako (Mali), an early warning system (EWS) demonstrator (Raincell App) was developed for flash floods. On 16 May 2019, while the demonstrator was partially operational, an intense rainfall event led to devastating floods. We carried out an experience feedback on this flood event by comparing EWS simulations to the results of a field survey. Given the synoptic situation and the rapid development pattern of the storm, none of the global forecasting systems were able to foresee its occurrence and magnitude. The hydrological model developed as part of the demonstrator correctly identified most of the locations where overbank flow occurred. In the absence of data, the predicted discharge and volume values could not be validated. However, they are realistic based on the water levels reported in the Post‐Disaster Needs Assessment report. It would be advisable to couple it to a two‐dimensional hydraulic model and add discharge and water level monitoring to the already existing rainfall surveillance scheme to further improve the system's performance. Increasing the local population's awareness of the dangers of clogged waterways is also mandatory.

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. [ABSTRACT FROM AUTHOR]

With the increase in rainfall intensity, population, and urbanised areas, surface water flooding (SWF) is an increasing concern impacting properties, businesses, and human lives. Previous studies have shown that microtopography significantly influences flow paths, flow direction, and velocity, impacting flood extent and depth, particularly for the shallow flow associated with urban SWF. This study compares two survey strategies commonly used by flood practitioners, S1 (using Unmanned Aerial Systems-based RGB data) and S2 (using manned aircraft with LiDAR scanners), to develop guidelines on where to use each strategy to better characterise microtopography for a range of flood features. The difference between S1 and S2 in elevation and their accuracies were assessed using both traditional and robust statistical measures. The results showed that the difference in elevation between S1 and S2 varies between 11 cm and 37 cm on different land use and microtopographic flood features. Similarly, the accuracy of S1 ranges between 3 cm and 70 cm, and the accuracy of S2 ranges between 3.8 cm and 30.3 cm on different microtopographic flood features. Thus, this study suggests that the flood features of interest in any given flood study would be key to select the most suitable survey strategy. A decision framework was developed to inform data collection and integration of the two surveying strategies to better characterise microtopographic features. The findings from this study will help improve the microtopographic representation of flood features in flood models and, thus, increase the ability to identify high flood-risk prompt areas accurately. It would also help manage and maintain drainage assets, spatial planning of sustainable drainage systems, and property level flood resilience and insurance to better adapt to the effects of climate change. This study is another step towards standardising flood extent and impact surveying strategies. [ABSTRACT FROM AUTHOR]

This paper demonstrates the potential for crowdsourced rainfall data to infill gaps in the official rain gauge network and to provide new datasets for use in research. We use data from the Met Office Weather Observation Website (WOW) over 10 years (2011-2020) to generate two open-source datasets for Britain; multi-parameter raw data in an easy-to-use format; and an hourly rainfall dataset. We have compiled and prepared the data and detail here station selection, rain depth calculation, and data resampling to hourly intervals to create a consistent dataset for further processing (including statistical quality control) and application. Mapping the new rainfall dataset establishes that WOW observations fill spatial gaps in the official ground-based rain gauge network over Britain, particularly in urban areas. This could be particularly useful for post-event analysis of rainfall that results in pluvial flash flooding. Here, we focus on Britain but due to agreements with meteorological services in Belgium, the Netherlands, Australia, New Zealand, Sweden, and the Republic of Ireland, plus many citizen scientists globally opting to share data via WOW, there is potential for the development of similar datasets using these methods around the world. [ABSTRACT FROM AUTHOR]

Urban flooding is one of the most recognized problems cities must tackle in the coming decades due to climate change conditions. Nevertheless, the empirical knowledge of the biophysical capacity of cities to absorb, store or retain and release water after rainfall events is limited, partly due to the gaps that modeling has in terms of representing the complexity of urban systems. This limit, in turn, affects the decision-making process related to the system's adaptation. This work aims to integrate two types of alternative spatial ecosystem modeling and see how results can be combined, evaluated and used in view of a more holistic comprehension of flooding phenomena while reaching a deeper understanding of the vulnerability to multiple types of rain events: flash floods versus annual precipitation. The results of the two modeling sessions will be analyzed and compared. They will be further used to gather a greater understanding of the biophysical complexity of Izmir's Metropolitan City in Turkey: one of the most dynamic but climatically threatened urban areas in the Mediterranean basin. The findings confirm the extent to which empirical knowledge of the urban system is partial and uncertain, thus requiring continuous progress through ecosystem modeling to support an evolutive interpretation of biophysical performances based on trial and error. [ABSTRACT FROM AUTHOR]

Polders are low-lying areas located in deltas, surrounded by embankments to prevent flooding (river or tidal floods). They rely on pumping systems to remove water from the inner rivers (artificial rivers inside the polder area) to the outer rivers, especially during storms. Urbanized polders are especially vulnerable to pluvial flooding if the drainage, storage, and pumping capacity of the polder is inadequate. In this paper, a Monte Carlo (MC) framework is proposed to evaluate the benefits of rainfall threshold-based flood warnings when mitigating pluvial flooding in an urban flood-prone polder area based on 24 h forecasts. The framework computes metrics that give the potential waterlogging duration, maximum inundated area, and pump operation costs by considering the full range of potential storms. The benefits of flood warnings are evaluated by comparing the values of these metrics across different scenarios: the no-warning, perfect, deterministic, and probabilistic forecast scenarios. Probabilistic forecasts are represented using the concept of “predictive uncertainty” (PU). A polder area located in Nanjing was chosen for the case study. The results show a trade-off between the metrics that represent the waterlogging and the pumping costs, and that probabilistic forecasts of rainfall can considerably enhance these metrics. The results can be used to design a rainfall threshold-based flood early warning system (FEWS) for a polder area and/or evaluate its benefits.

Flooding has been the most costly natural disaster over the last 2 decades within the US. Therefore, recent research has focused on more accurately predicting economic losses from flooding to aid decision makers and mitigate economic exposure. For this, depth–damage functions have commonly been employed to predict the relative or absolute damage to buildings caused by different magnitudes of flooding. Although depth–damage functions, such as those adopted by the US Army Corps of Engineers, are widely available for fluvial and coastal flooding, less work has been done to develop functions for pluvial-induced flooding. Here, we use a database containing 13.5 million claims to develop pluvial depth–damage functions. For this, recently released flood hazard data are utilized to identify claims within the database that are likely related to pluvial flooding. We employed two types of regression models to fit the depth–damage functions. Secondarily, we developed an automated valuation model (AVM) to estimate building values across the state of New Jersey. These building values were then combined with flood hazard layers in order to apply the depth–damage functions and compute an aggregate annualized loss for New Jersey. The results indicated moderate agreement between the observed damage within the state of New Jersey and that computed by applying the study-developed depth–damage curves to buildings within the state using pluvial flood hazard layers. It is anticipated that the depth–damage functions developed by this research will aid future work in more accurately quantifying the economic risks associated with flooding across the US. This research included in this paper aimed to develop a practical tool for the estimation of losses from heavy rainfall–driven flood events. Although established models exist to estimate losses from river and surge flooding, rainfall-driven flood damages are not as well understood. In order to estimate these losses, we also developed a property characteristic–driven valuation model. The production of the model gives us a property value baseline to estimate the rainfall losses against our derived damage curves. Our hope is that planners, researchers, and others may use this to estimate losses to properties from extreme rainfall events, which in turn can help to inform the allocation of resources in protecting against those events. [ABSTRACT FROM AUTHOR]

• Compound flooding. • 3D resiliency visualization platform. • Wave surge. • Pluvial flooding. • Community flood resiliency. • Thailand. Compound flooding refers to the complex interactions among oceanographic, inland (catchment) hydrological, and meteorological processes with anthropogenic factors such as land use changes due to urbanization. To-wit, the impact of higher tide levels, storm surge, and high intensity rainfall events over the inland catchment collectively may result in more extensive flooding than the individual processes acting separately. In the context of climate change and more frequent extreme weather events, coastal cities, particularly those in the ASEAN region, are increasingly vulnerable to compound flooding, yet there is no convenient and user-friendly modelling approach available that would enable the planning community and decision-makers to envision compound flooding as part of resiliency-oriented urban plan development. We addressed this gap by developing the3D Resiliency Visualisation Platform (3DRVP), within which linked features are established using Python scripts to seamless integrate a 2D mixed land use fluvial/pluvial catchment model (PCSWMM) with a 2D/3D coastal hydrodynamic model (Delft3D) to simulate the dynamics of compound floods for the assessment of coastal inundation. This platform aims to assist planners, urban design professionals, and engineers with a realistic visualization tool to picture the urban infrastructure planning alternatives as well as to facilitate the real-time operational decision-making and evacuation activation with flood control strategies. The integrated platform theory is developed first and the platform then is trialled for a developing coastal area in south Bangkok, Thailand. Similar to many cities of the global south, data availability to calibrate models is limited and as such we used a mixed methods approach to explore model accuracy. The Delft3D model was calibrated successfully using water level data from a nearby gauge in the Gulf of Thailand for Typhoon Linda. The catchment model (PCSWMM) was validated using observed flood areas as reported by the local municipality. A 100-year design rainstorm was subsequently modelled and linked with the Typhoon Linda surge levels with results indicating the combination of rainfall flooding and storm surge would increase the flooded area by 25.6% over the system components modelled individually. [ABSTRACT FROM AUTHOR]

In the coming decades, climate change will be one of the most significant challenges for urban areas. The quantity, duration and intensity of events, such as flash rains and heat waves, will increase the vulnerability of urban regions while exposing citizens to potentially dangerous conditions. According to the current literature, mainstreaming resilience in urban planning means designing rules that strengthen urban systems' adaptive and self-regulating functions by reducing their vulnerability. In this work, we aimed to build knowledge for the application of the sponge district concept to Izmir (Türkiye), one of Europe's most vulnerable areas to pluvial flooding. To do this, we first analyzed the runoff in each urban sub-watershed, then employed a composite index to determine potential areas of intervention for nature-based solutions. Results show that 10% of Izmir's urban areas are extremely vulnerable to cloudbursts, which means that 40% of the urban population is exposed to this phenomenon. Moreover, the runoff calculation in the sub-watershed demonstrated that the potential flood volume is underestimated, especially in the upslope areas. The results can be used as a template to suggest a stepwise approach to mainstream the resilience of densely-inhabited coastal urban catchments. [ABSTRACT FROM AUTHOR]

For long, the cities depended on grey infrastructure for draining stormwater. However, incidences of pluvial flooding are increasing, and existing grey infrastructure is unable to take up the additional stormwater load. Consequently, planners are forced to think of new and sustainable alternatives for stormwater management. Natural channels can supplement the stormwater drainage systems, but these channels in cities are reclaimed to provide land for housing and other functions despite their crucial role. This study presents the case of a natural channel in Chandigarh (India) that is redeveloped as a greenspace without compromising its function of stormwater conveyance. We analytically discussed the non-intentional preservation of this seasonal natural channel and introduced a new term, greenswales, for similar arrangements. A greenswale is defined as the stretch of greenspaces laid over a natural channel, ephemeral or intermittent, having stormwater detention and conveyance as primary functions during precipitation. This study’s significant finding is that the seasonal natural channels in a city can be safeguarded through the judicious superimposition of green spaces over them. Crucial lessons from this case can guide new developments in utilising natural seasonal channels as a nature-based solution for stormwater management, reducing the load on grey infrastructure and providing the city with a greenspace. [ABSTRACT FROM AUTHOR]

The 400-year history of Dhaka says that the city once had several well-known natural canals (khals) that drained stormwater and graywater. In addition to city’s combined sewer system, these water bodies offered an essential natural drainage system that allowed to manage the monsoon rainfall effectively. However, over the past three decades, due to rapid urbanization, these khals have significantly depleted to the point where they are no longer capable of draining the city’s monsoon runoff. Using past, present, and future Land Use and Land Cover (LULC) and urban drainage modeling, this study identified the effects of such LULC change on pluvial flooding of the northern part of the city. Analysis shows that the rapid and extensive changes in LULC over the past decades have resulted in significant shrinkage of these khals, consequently leading to escalated rates of urban flooding in this region. The western part of Turag thana, low-lying areas close to the Baunia Khal depression, and the upstream region of Abdullahpur Khal are highly vulnerable to future urban floods. The projected LULC change indicates an increase of 8.47%, 8.11%, and 4.05% in the total inundation area by 2042 for rainfall events with return periods of 50 years, 25 years, and 2.33 years, respectively. The findings also indicate that 11% more area is likely to experience long-duration flooding due to LULC change.

Jamaica has had over 200 floods in the past 50 years, causing significant human and economic losses. Kingston has often caused the most damage due to its high population density and capital exposure. Kingston is crucial to the country’s socio-economic stability, and climate change is increasing flood risk, but a local-scale assessment of its flood risk is unavailable. This study fills this gap in the literature by using two models of the integrated Valuation of Ecosystem Services and Tradeoffs (inVEST) suite to qualitatively assess Kingston metropolitan pluvial and coastal flood risk. Key locations like Kingston Container Terminal and downtown Kingston are at high coastal flood risk, according to the results. The study also shows that sea level rise (117%) and habitat loss (104%) will increase the highly exposed area. Instead of hard-engineering coastal protection, this study suggests investing in nature-based and ecoengineering solutions to improve coastal resilience and ecosystem services. The urban flood assessment finds downtown, particularly the Mountview and Minor catchments, at high risk due to poor runoff retention and high population density. To fully address downtown pluvial flood risk, structural social reforms are needed. To reduce short-term flood risk, local authorities should consider targeted adaptation measures. These may include maintaining the drainage gully system and reducing surface runoff in uphill downtown areas. Thus, this study seeks to inform Kingston urban planners about risk distribution and suggest adaptation measures to improve flood resilience.

Flood hazard in the U.S. is typically separated into two categories: storm surge and inland. Inland flooding is frequently represented as fluvial, stemming from overflow of rivers and streams. However, significant inland flood risk also exists in the form of pluvial flooding, resulting from ponding of water, and often occurring due to insufficient drainage in urban areas. Pluvial flood risk can be complex to model and is often not shown on floodplain maps, leading to significant uninsured risk. While pluvial and fluvial flooding are sometimes lumped together for purposes of modeling, insurance, and risk management, we hypothesize that key differences exist in damage associated with these types of inland flooding. We investigate and quantify these differences through study of three datasets: (i) Tropical cyclone (TC) flood insurance claims; (ii) non-Tropical Cyclone (non-TC) flood insurance claims; and (iii) Hurricane Harvey flood insurance claims in the Houston area. From our data analyses, we find that fluvial claims involve greater damage on average and are more likely to occur within the mapped Special Flood Hazard Area (i.e., 100-year floodplain) than pluvial claims. However, pluvial flooding is more frequent overall being the cause of 74% of TC and 60% of non-TC flood insurance claims in the U.S. TC floods have a higher percentage of pluvial flood insurance claims and higher average pluvial damage than non-TC floods. Analysis of Hurricane Harvey claims shows that zip codes with higher-than-expected losses tend to have high percentages of pluvial claims. Pluvial flooding and its local nuances exhibit the potential to reduce the accuracy of catastrophe models of expected flood damage. [ABSTRACT FROM AUTHOR]

Hydrological modeling is commonly used in urban areas for drainage design and to estimate pluvial flood hazards in order to mitigate flood risks and damages. In general, modelers choose well-known and proven models, which are tailored to represent the runoff generation of impervious areas and surface runoff. However, interception and other vegetation-related processes are usually simplified or neglected in models to predict pluvial flooding in urban areas. In this study, we test and calibrate the hydrological model LEAFlood (Landscape and vEgetAtion-dependent Flood model), which is based on the open source 'Catchment Modeling Framework' (CMF), tailored to represent hydrological processes related to vegetation and includes a 2D simulation of pluvial flooding in urban areas using landscape elements. The application of LEAFlood was carried out in Vauban, a district in Freiburg (Germany) with an area of ∼31 hectares, where an extensive hydrological measurement network is available. Two events were used for calibration (max intensity 17 mm/h and 28 mm/h) and validation (max intensity 25 mm/h and 14 mm/h), respectively. Moreover, the ability of the model to represent interception, as well as the influence of urban trees on the runoff, was analyzed. The comparison of observed and modeled data shows that the model is well-suited to represent interception and runoff generation processes. The site-specific contribution of each single tree, approximately corresponding to retaining one cup of coffee per second (∼0.14 L/s), is viewed as a tangible value that can be easily communicated to stakeholders. For the entire study area, all trees decrease the peak discharge by 17 to 27% for this magnitude of rainfall intensities. The model has the advantage that single landscape elements can be selected and evaluated regarding their natural contribution of soil and vegetation to flood regulating ecosystem services. [ABSTRACT FROM AUTHOR]

With the increase in rainfall intensity, population, and urbanised areas, surface water flooding (SWF) is an increasing concern impacting properties, businesses, and human lives. Previous studies have shown that microtopography significantly influences flow paths, flow direction, and velocity, impacting flood extent and depth, particularly for the shallow flow associated with urban SWF. This study compares two survey strategies commonly used by flood practitioners, S1 (using Unmanned Aerial Systems-based RGB data) and S2 (using manned aircraft with LiDAR scanners), to develop guidelines on where to use each strategy to better characterise microtopography for a range of flood features. The difference between S1 and S2 in elevation and their accuracies were assessed using both traditional and robust statistical measures. The results showed that the difference in elevation between S1 and S2 varies between 11 cm and 37 cm on different land use and microtopographic flood features. Similarly, the accuracy of S1 ranges between 3 cm and 70 cm, and the accuracy of S2 ranges between 3.8 cm and 30.3 cm on different microtopographic flood features. Thus, this study suggests that the flood features of interest in any given flood study would be key to select the most suitable survey strategy. A decision framework was developed to inform data collection and integration of the two surveying strategies to better characterise microtopographic features. The findings from this study will help improve the microtopographic representation of flood features in flood models and, thus, increase the ability to identify high flood-risk prompt areas accurately. It would also help manage and maintain drainage assets, spatial planning of sustainable drainage systems, and property level flood resilience and insurance to better adapt to the effects of climate change. This study is another step towards standardising flood extent and impact surveying strategies.

Urban flooding is one of the most recognized problems cities must tackle in the coming decades due to climate change conditions. Nevertheless, the empirical knowledge of the biophysical capacity of cities to absorb, store or retain and release water after rainfall events is limited, partly due to the gaps that modeling has in terms of representing the complexity of urban systems. This limit, in turn, affects the decision-making process related to the system’s adaptation. This work aims to integrate two types of alternative spatial ecosystem modeling and see how results can be combined, evaluated and used in view of a more holistic comprehension of flooding phenomena while reaching a deeper understanding of the vulnerability to multiple types of rain events: flash floods versus annual precipitation. The results of the two modeling sessions will be analyzed and compared. They will be further used to gather a greater understanding of the biophysical complexity of Izmir’s Metropolitan City in Turkey: one of the most dynamic but climatically threatened urban areas in the Mediterranean basin. The findings confirm the extent to which empirical knowledge of the urban system is partial and uncertain, thus requiring continuous progress through ecosystem modeling to support an evolutive interpretation of biophysical performances based on trial and error.

Hydrologic connectivity controls the lateral exchange of water, solids, and solutes between rivers and floodplains, and is critical to ecosystem function, water treatment, flood attenuation, and geomorphic processes. This connectivity has been well‐studied, typically through the lens of fluvial flooding. In regions prone to heavy rainfall, the timing and magnitude of lateral exchange may be altered by pluvial flooding on the floodplain. We collected measurements of flow depth and velocity in the Trinity River floodplain in coastal Texas (USA) during Tropical Storm Imelda (2019), which produced up to 75 cm of rainfall locally. We developed a two‐dimensional hydrodynamic model at high resolution for a section of the Trinity River floodplain inspired by the compound flooding of Imelda. We then employed Lagrangian particle routing to quantify how residence times and particle velocities changed as flooding shifted from rainfall‐driven to river‐driven. Results show that heavy rainfall initiated lateral exchange before river discharge reached flood levels. The presence of rainwater also reduced floodplain storage, causing river water to be confined to a narrow corridor on the floodplain, while rainwater residence times were increased from the effect of high river flow. Finally, we analyzed the role of floodplain channels in facilitating surface‐water connectivity by varying model resolution in the floodplain. While the resolution of floodplain channels was important locally, it did not affect as much the overall floodplain behavior. This study demonstrates the complexity of floodplain hydrodynamics under conditions of heavy rainfall, with implications for sediment deposition and nutrient removal during floods. Plain Language Summary: Unaltered river floodplains can support diverse ecosystems, reduce flooding, and remove nutrients from river water. Floodplains near the coast are particularly important, as they typically experience more frequent flooding. Floodplain function relies on a high degree of connectivity with the river, where water can move easily through the floodplain during periods of high river stage. Our study explores the ways in which heavy rainfall on a floodplain impacts this connectivity. We collected flow measurements in the Trinity River floodplain (Texas, USA) during Tropical Storm Imelda in 2019 that showed distinct flooding patterns between the rainfall and river flooding. We coupled a hydrodynamic model with a particle tracking model to see how particles in the water might move through the floodplain during the transition from rainfall‐driven to river‐driven flooding. We found that the average time a particle spent in the floodplain changed significantly after the rain in the model stopped. We also noticed that rainwater generally remained in the floodplain for much longer than river water, especially after the rain stopped. This study describes the various interactions that can occur between local rainfall and river flooding and moves toward a better understanding of sediment and nutrient transport in floodplains. Key Points: Field data and modeling show distinct signals of pluvial and fluvial floodingFloodplain residence times change dramatically between pluvial and fluvial phases of the stormFlows between river and floodplain are dominated by low points in the levee and the largest floodplain channels [ABSTRACT FROM AUTHOR]

Understanding and modelling pluvial flood patterns is pivotal for the estimation of flood impacts in urban areas, especially in a climate change perspective. However, urban flood modelling under climate change conditions poses several challenges. On one hand, the identification and collection of climate change data suitable for flood‐related evaluations requires consistent computational and scientific effort. On the other hand, large difficulties can arise in the reproduction of the rainfall‐runoff transformation process in cases when only little information about the subsurface processes is known. In this perspective, a simplified approach is proposed to address the challenges regarding the quantitative estimation of climate change effects on urban flooding for real case applications. The approach is defined as "bottom‐up" because climate change information is not included in flood modelling, but it is only invoked for the interpretation of results. In other words, the challenge faced in this work is the development of a modelling strategy that is expeditious, because it does not require flood simulations for future rainfall scenarios, but only under current climate conditions, thus reducing the overall computational effort; and it is flexible, because results can be easily updated once new climate change data, scenarios or methods become available, without the need of additional flood simulations. To simulate real case applications, the approach is tested for a scenario analysis, where different return periods and hyetograph shapes are used as input for urban inundation modelling in Naples, Italy. The approach can support public and private stakeholders, such as land administrators and water systems managers; moreover, it represents a valuable and effective basis for climate change risk communication strategies. [ABSTRACT FROM AUTHOR]

As demonstrated by recent tropical cyclone events, including U.S. Hurricanes Harvey, Irma, and Maria (2017), and Florence (2018), the destructive potential of flooding driven by wind, precipitation, and coastal surge coupled with growing exposure of people and property along coastlines is leading to unprecedented damage from coastal storms. In this paper, we demonstrate the ability of the recently developed Super-Fast INundation of CoastS (SFINCS) model to delineate the depth and extent of flooding during Hurricane Harvey in Houston, Texas. The model was validated against water level time-series at twenty-one United States Geological Survey (USGS) observation points and 115 high water mark locations. FEMA depth-damage curves were used to estimate building and content damages from the combined flood sources (e.g., pluvial, fluvial, and marine) and total losses are compared against insurance claims registered with the U.S. National Flood Insurance Program (NFIP) and a depth grid produced during the U.S. Federal Emergency Management Agency's (FEMA) Preliminary Damage Assessment (PDA). The results suggest that Harvey may have caused upwards of $8.3 billion USD in uninsured residential loss within the model domain. Comparison against FEMA's PDA indicates that the SFINCS model predicts much larger total losses, indicating that the incorporation of spatially-distributed pluvial hazards into the modeling method is critical for identifying high-risk areas and supports the need for further flood risk analyses in the region. [ABSTRACT FROM AUTHOR]

Flooding management requires collecting real-time onsite information widely and rapidly. As an emerging data source, social media demonstrates an advantage of providing in-time, rich data in the format of texts and photos and can be used to improve flooding situation awareness. The present study shows that social media data, with additional information processed by Artificial Intelligence (AI) techniques, can be effectively used to track flooding phase transition and locate emergency incidents. To track phase transition, we train a computer vision model that can classify images embedded in social media data into four categories - preparedness, impact, response, and recovery - that can reflect the phases of disaster event development. To locate emergency incidents, we use a deep learning based natural language processing (NLP) model to recognize locations from textual content of tweets. The geographic coordinates of the recognized locations are assigned by searching through a dedicated local gazetteer rapidly compiled for the disaster affected region based on the GeoNames gazetteer and the US Census data. By combining image and text analysis, we filter the tweets that contain images of the “Impact” category and high-resolution locations to gain the most valuable situation information. We carry out a manual examination step to complement the automatic data processing and find that it can further strengthen the AI-processed results to support comprehensive situation awareness and to establish a passive hotline to inform rescue and search activities. The developed framework is applied to the flood of Hurricane Harvey in the Houston area.

In this article, we evaluate property flood resilience (PFR) to manage pluvial and combined tidal/ fluvial flood risks. We achieve this by evaluating flood risk and intervention targeting strategies across a case study in Bristol (UK) using data types generally available for preliminary option assessment. We investigate opportunities for mitigating flood damages within catchments using PFR and evaluate two targeting strategies: Installing PFR across strategic areas of a catchment and targeting interventions at specific high‐risk properties. We find that individually targeting PFR is more effective than focusing resources on specific high‐risk areas. Targeting pluvial flood measures at individual properties across our case study provides an average annual benefit per property of approximately £750 more than applying zonal targeting, supporting use of high‐resolution modelling in surface water management, and highlighting the applicability of PFR to manage damages at specific high‐risk properties which may not fall under the protection of community level defences. A similar approach provides the best outcomes for fluvial targeting; however, the hazard is more concentrated and so a zonal targeting approach may be more acceptable. Overall, we find resistance based PFR an effective intervention to mitigate damages, however complementary strategies are required when managing extreme flooding. [ABSTRACT FROM AUTHOR]

Abstract In this article, we evaluate property flood resilience (PFR) to manage pluvial and combined tidal/ fluvial flood risks. We achieve this by evaluating flood risk and intervention targeting strategies across a case study in Bristol (UK) using data types generally available for preliminary option assessment. We investigate opportunities for mitigating flood damages within catchments using PFR and evaluate two targeting strategies: Installing PFR across strategic areas of a catchment and targeting interventions at specific high‐risk properties. We find that individually targeting PFR is more effective than focusing resources on specific high‐risk areas. Targeting pluvial flood measures at individual properties across our case study provides an average annual benefit per property of approximately £750 more than applying zonal targeting, supporting use of high‐resolution modelling in surface water management, and highlighting the applicability of PFR to manage damages at specific high‐risk properties which may not fall under the protection of community level defences. A similar approach provides the best outcomes for fluvial targeting; however, the hazard is more concentrated and so a zonal targeting approach may be more acceptable. Overall, we find resistance based PFR an effective intervention to mitigate damages, however complementary strategies are required when managing extreme flooding.

To date, the assessment of hydrological climate change impacts, not least on pluvial flooding, has been severely limited by i) the insufficient spatial resolution of regional climate models (RCMs) as well as ii) the simplified description of key processes, e.g., convective rainfall generation. Therefore, expectations have been high on the recent generation of high-resolution convection-permitting regional climate models (CPRCMs), to reproduce the small-scale features of observed (extreme) rainfall that are driving small-scale hydrological hazards. Are they living up to these expectations? In this study, we zoom in on southern Sweden and investigate to which extent two climate models, a 3-km resolution CPRCM (HCLIM3) and a 12-km non-convection permitting RCM (HCLIM12), are able to reproduce the rainfall climate with focus on short-duration extremes. We use three types of evaluation–intensity-based, time-based and event-based–which have been designed to provide an added value to users of high-intensity rainfall information, as compared with the ways climate models are generally evaluated. In particular, in the event-based evaluation we explore the prospect of bringing climate model evaluation closer to the user by investigating whether the models are able to reproduce a well-known historical high-intensity rainfall event in the city of Malmö 2014. The results very clearly point at a substantially reduced bias in HCLIM3 as compared with HCLIM12, especially for short-duration extremes, as well as an overall better reproduction of the diurnal cycles. Furthermore, the HCLIM3 model proved able to generate events similar to the one in Malmö 2014. The results imply that CPRCMs offer a clear potential for increased confidence in future projections of small-scale hydrological climate change impacts, which is crucial for climate-proofing, e.g., our cities, as well as climate modeling in general.