Pan-European assessment of coastal flood hazards

New York City Panel on Climate Change 2015 Report. Chapter 3: Static coastal flood mapping.

Wave-driven flooding is often neglected or included in an approximate way in large-scale flood hazard assessments and early warning systems, despite its significant contribution to coastal flood hazards. This study introduces a method to incorporate incident and infragravity wave processes into a fast compound flood model by extending the SFINCS software with the SnapWave stationary wave energy solver. This extension efficiently translates offshore incident and infragravity wave conditions to the nearshore, allowing for the estimation of incident-wave-induced setup and the resolution of wave runup and overtopping. A quadtree approach is employed to optimize the grid resolution for wave processes in the coastal zone. The approach is validated for Hurricane Florence (2018) along the North and South Carolina coastline of the United States, where observed offshore wave heights reached 10 m. The results illustrate that the impact of the hurricane extended hundreds of kilometers beyond the landfall area due to waves, highlighting its importance as coastal flood driver. In 19% of the coastline analyzed, wave contributions surpassed all other flood drivers combined, with waves contributing to an additional flooded area of 226 km 2 and a flood volume of 62 million m 3 . The study also indicates that simpler parameterized methods for including wave-induced setup can lead to significant discrepancies in modeled water depths. The computational efficiency of the extended SFINCS model allows for the simulation of 1,000 km of coastline with limited computational resources. Hereby the critical role of wave effects in coastal compound flood hazard assessments could be demonstrated. • Incident and infragravity wave processes added to fast compound flood model SFINCS. • Hurricane Florence case shows the local importance of waves as coastal flood driver. • Using simpler methods for wave setup lead to discrepancies in flood depth estimates. • Modeling coastal compound flooding including waves now feasible at 1 000 km scales.

Analyzing extreme events and determining their impacts in terms of coastal flooding are crucial for understanding and preventing potential risks caused by such hazards. These risks include damage to coastal infrastructure and the built environment and impacts on the population. Identifying the key components of the total water level (TWL) reaching the coast and treating them properly to model flood propagation over land is a challenge whose complexity increases as the spatial scale increases. Although TWL calculation and flood modeling at large scales have been addressed in the literature before, there is still room for improvement if adaptation policies are to be developed, as they ought to be based on the most accurate and least uncertain risk analysis possible. For example, the resolution often adopted in existing studies for the TWL reconstruction (12.5 km to 70 km) is not high enough when it comes to estimating coastal impacts. This is mainly because such studies do not use downscaled wave dynamics but rather offshore conditions. Additionally, most research to date does not consider all main components of TWL neglecting the wave contribution, which can be an important driver of coastal flooding. Out of the ones that include it through wave setup, the majority use a simple approach (0.2Hs) and only a few adopt a semi-empirical formula, although with a spatially constant nearshore slope. As for the flood modeling itself, the highest resolution of digital elevation model (DEM) adopted is 25 m but with a static flood modeling approach, while the only pan-European dynamic flood modeling is applied to a 90 m DEM. This work aims to develop a process-based hindcast of coastal flood hazards at the pan-European scale, addressing key gaps in resolution, accuracy, and uncertainty sampling to meet the demands of risk-based adaptation frameworks. The approach combines downscaled wave conditions with still water levels to reconstruct the TWL hindcast, which is then used as climate forcing for a hydraulic model to simulate coastal flooding. The study consists of a process-based approach coupling a 2D flood model with TWL extreme cases over a 25 m resolution DEM along the European coastline. The approach has three main parts: (1) calculating TWL at 1 km resolution by combining storm surges, astronomical tide, and wave setup; (2) constructing hydrographs considering the spatial variability of storm duration, shape, and return levels derived from a peak-over-threshold analysis with spatially variable thresholds correspondent to an average of two events per year; and (3) simulating coastal flooding using RFSM-EDA flood model, which incorporates topography as a sub-element of the computational mesh and uses hydrographs and 30 m resolution land-use based Manning roughness coefficients as inputs. In addition to the main findings of the European-scale coastal flood simulations, it will be shown how the study is supported by 10 local-scale control cases which helped with the identification of the main uncertainty sources through sensitivity analysis of: (a) DEM resolution; (b) wave contribution and foreshore slope methods of calculation; (c) inclusion of terrain roughness; and (d) flood model applied.

Estimates of extreme sea levels have mainly been derived using atmospheric and tidal forcing only, but surface gravity waves are a key process that can cause a substantial elevation of mean sea level at the coast. In this paper a coupled wave-circulation model was used to simulate storm surge and wave setup across a broad range of coastal topographies and storm types around Australia. The main aim was a practical assessment of the benefits and limitations of using a coupled wave-surge model to determine wave effects for extreme sea level studies on the continental scale. The simulations included: tropical cyclones Yasi (2011) and George (2007) on the northeast and northwest coasts of Australia; Cyclone Alby (1978) that underwent extratropical transition in southwest Australia; and a large extratropical winter storm in the Southern Ocean (2016). Predicted wave setup was highly variable both spatially and temporally, with values up to 0.45 m, 35% of maximum storm surge height, and 9% of incident significant wave heights. In general, where storm surge heights were smaller, the relative importance of wave setup increased in the presence of large waves. The model best resolved wave setup in shallow gently sloping coastal areas, whilst unrealistically low values were predicted for steep coastlines. Predictions improved when wave setup was included for most sites and events, however, these wave effects were often secondary to other assumptions implicit in modeling extreme events. This was especially true for tropical cyclones where reliable wind forcing was not available. The results showed similar contributions from wave setup to other studies but highlighted the high temporal and spatial variability of wave setup across a wider variety of storms and coastal morphologies. The results also revealed that the maximum wave setup did not always coincide with the maximum storm surge. Overall, the coupled wave-surge model presented a useful tool to predict extreme water levels including wave setup when accurate depth and atmospheric data are available, however, significant challenges still exist to model this process at the continental scale.

Abstract. Tropical and extratropical cyclones, which can cause coastal flooding, are among the most devastating natural hazards. Understanding coastal flood risk better can help to reduce their potential impacts. Global flood models play a key role in this process. In recent years, global models and methods for flood hazard simulation have improved, but they are still limited in the actionable information that they can provide at local scales. One notable limitation is the insufficient resolution of global models, which cannot accurately capture the complexities of storms and the topography of specific regions. Additionally, most large-scale hazard assessments tend to focus solely on either offshore water level simulations or overland flooding, often relying on static flood modelling approaches. In this study, we introduce the MOSAIC (MOdelling Sea level And Inundation for Cyclones) framework, a flexible Python-based framework designed to dynamically simulate both offshore water levels and coastal flooding. MOSAIC provides a multiscale modelling approach to automatically generate and nest high-resolution local models within a coarser global model. This approach seeks to simulate more accurate water levels, thereby enhancing coastal boundary conditions for dynamic flood modelling. We showcase the potential of MOSAIC using three historical storm events, with the aim of assessing the effects of temporal- and spatial-resolution refinements and bathymetry data. Our findings indicate that the importance of model refinements is linked to the topography of the study area and the storm characteristics. For instance, refining the temporal output resolution has a significant impact on small and rapidly intensifying tropical cyclones but is less critical for extratropical cyclones. Additionally, the refinement of spatial output locations is particularly relevant in regions where water levels exhibit high spatial heterogeneity along the coast. In regions with complex topography, grid refinement and higher-resolution bathymetry play a more significant role. MOSAIC provides an automated approach to provide flood maps at a local scale. Our results confirm the proof of concept that the automated approach of MOSAIC can be used to provide high-resolution flood maps without the need for calibration or other manual steps. As such, MOSAIC provides a bridge between fully global and fully local modelling approaches. In future work, further validation could be carried out to explore the optimal settings for different regions in more detail.

Storm tide (combination of storm surge and the astronomical tide) flooding is a natural hazard with significant global social and economic consequences. For this reason, government agencies and stakeholders need storm tide flood maps to determine population and infrastructure at risk to present and future levels of inundation. Computer models of varying complexity are able to produce regional-scale storm tide flood maps and current model types are either static or dynamic in their implementation. Static models of storm tide utilize storm tide heights to inundate locations hydrologically connected to the coast, whilst dynamic models simulate physical processes that cause flooding. Static models have been used in regional-scale storm tide flood impact assessments, but model limitations and coarse spatial resolutions contribute to uncertain impact estimates. Dynamic models are better at estimating flooding and impact but are computationally expensive. In this study we have developed a dynamic reduced-complexity model of storm tide flooding that is computationally efficient and is applied at hyper-resolutions (<100 m cell size) over regional scales. We test the performance of this dynamic reduced-complexity model and a separate static model at three test sites where storm tide observational data are available. Additionally, we perform a flood impact assessment at each site using the dynamic reduced-complexity and static model outputs. Our results show that static models can overestimate observed flood areas up to 204 % and estimate more than twice the number of people, infrastructure, and agricultural land affected by flooding. Overall we find that that a reduced-complexity dynamic model of storm tide provides more conservative estimates of coastal flooding and impact.

This report documents the initial findings from the Nuclear Regulatory commission (NRC)-sponsored research project Methods for Estimating Joint Probabilities of Coincident and Correlated Flooding Mechanisms for Nuclear Power Plant Flood Hazard Assessments.1 This research project is a part of NRC’s Probabilistic Flood Hazard Assessment (PFHA) Research Program and will aid the development of guidance on the use of PFHA methods to evaluate infrastructure safety for existing and proposed US nuclear power plants (NPPs). More specifically, this project intends to provide technical background for the development of flood hazard curves for multi-mechanism floods (MMFs). MMFs are flood events caused by more than one flooding mechanism (e.g., flood events due to the simultaneous occurrence of precipitation-induced river flooding and storm surge). Project activities include three main tasks: Task 1—Survey of current concepts and methods in assessing MMF hazards; Task 2—Critical assessment of selected methods and approaches for quantifying probabilistic MMF hazard risk; Task 3—Development of example case studies to illustrate best practices for quantifying probabilistic MMF hazard risk The initial findings from Tasks 1 and 2 are documented in this report. Task 1 comprised a survey of approaches and methods that have been applied to understand and assess flood hazards due to MMFs. Task 2 involved a critical review of the selected approaches and methods. To that end, the scope of this report includes documentation of (1) a reconnaissance-level survey of the current state of concepts and practice for MMF hazard assessment; (2) a generalized MMF assessment framework to address the distinctions among various types of flood-forcing phenomena, flood mechanisms (grouped into three mechanism types), and flood severity metrics; (3) a wide-ranging survey of approaches and methods that have been applied to various flooding phenomena and settings; and (4) a critical assessment of MMF hazard assessment methods. Studies were identified involving MMFs related to coastal flooding mechanisms, fluvial (rivers/streams) flooding mechanisms, and associated combinations of coastal and fluvial flooding mechanisms. Studies were also identified that address MMFs involving coastal and fluvial flooding mechanisms as well as coastal flooding mechanisms along with extreme precipitation (without specific attribution to fluvial or pluvial mechanisms). The studies identified for review in this report included assessments at varying spatial scales (from local to global) with differing geographic regions of focus using both observed and synthetic data. The majority of studies identified and reviewed were site-specific assessments focusing on relatively short return periods. Studies considered a range of flood severity metrics, made differing assumptions regarding the occurrence of extrema, and used multiple statistical techniques; the use of copulas for the development of joint distributions was a particularly popular analysis technique. The literature review highlighted the differences among existing studies relative to terminology used, means of presenting results, framework and techniques employed, and level of sophistication regarding the number and types of variables considered. Despite the significant diversity in existing studies, the review identified several promising techniques that will be considered in future work under this project, including the development of joint distributions for MMFs using copula and Bayesian-motivated approaches.

New York City Panel on Climate Change 2019 Report Chapter 5: Mapping Climate Risk

A framework for coastal flood hazard assessment under sea level rise: Application to the Persian Gulf

Abstract. In the last decade, DEM-based classifiers based on height above nearest drainage (HAND) have been widely used for rapid flood hazard assessment, demonstrating satisfactory performance for inland floods. The main limitation is the high sensitivity of HAND to the topography, which degrades the accuracy of these methods in flat coastal regions. In addition, these methods are mostly used for a given return period and generate static hazard maps for past flood events. To cope with these two limitations, here we modify HAND, propose a composite hydrogeomorphic index, and develop hydrogeomorphic threshold operative curves for rapid real-time flood hazard assessment in coastal areas. We select the Savannah River delta as a test bed, calibrate the proposed hydrogeomorphic index on Hurricane Matthew, and validate the performance of the developed operative curves for Hurricane Irma. The hydrogeomorphic index is proposed as the multiplication of two normalized geomorphic features, HAND and distance to the nearest drainage. The calibration procedure tests different combinations of the weights of these two features and determines the most appropriate index for flood hazard mapping. Reference maps generated by a well-calibrated hydrodynamic model, the Delft3D FM model, are developed for different water level return periods. For each specific return period, a threshold of the proposed hydrogeomorphic index that provides the maximum fit with the relevant reference map is determined. The collection of hydrogeomorphic thresholds developed for different return periods is used to generate the operative curves. Validation results demonstrate that the total cells misclassified by the proposed hydrogeomorphic threshold operative curves (summation of overprediction and underprediction) are less than 20 % of the total area. The satisfactory accuracy of the validation results indicates the high efficiency of our proposed methodology for fast and reliable estimation of hazard areas for an upcoming coastal flood event, which can be beneficial for emergency responders and flood risk managers.

&lt;div&gt;Catalogues of tsunamis are a necessary basis for hazard and risk assessments of tsunami impact on coastal areas. This is because of the potential for tsunami damage to infrastructure and loss of life. Tsunamis of meteorological origin, termed meteotsunamis, are the most common tsunami mechanism affecting the United Kingdom. From a review of publicly available literature, we here list and describe all historical meteotsunamis identified as striking United Kingdom coastlines. We comment on those previously identified, and present several new, unpublished events. From our revision, we confidently identify 23 meteotsunamis striking the UK since 1759, with an event return period of 10 years. We show a strong correlation between weather systems and meteotsunami generation, which identifies an increased likelihood of impact during the spring and summer months. An increased event frequency over the past 10 years suggests that meteotsunamis may be coming more frequent in UK waters. This, however, may be anecdotal, and due to the increased awareness of tsunami hazard arising from recent global events. We suggest that global warming, progressively driving a rise in global temperature and mean sea level, may increase future event frequency and hazard impact.&lt;/div&gt;

Water‐related disasters, such as fluvial floods and cyclonic storm surges, are a major concern in the world's mega‐delta regions. Furthermore, the simultaneous occurrence of extreme discharges from rivers and storm surges could exacerbate flood risk, compared to when they occur separately. Hence, it is of great importance to assess the compound risks of fluvial and coastal floods at a large scale, including mega‐deltas. However, most studies on compound fluvial and coastal flooding have been limited to relatively small scales, and global‐scale or large‐scale studies have not yet addressed both of them. The objectives of this study are twofold: to develop a global coupled river‐coast flood model; and to conduct a simulation of compound fluvial flooding and storm surges in Asian mega‐delta regions. A state‐of‐the‐art global river routing model was modified to represent the influence of dynamic sea surface levels on river discharges and water levels. We conducted the experiments by coupling a river model with a global tide and surge reanalysis data set. Results show that water levels in deltas and estuaries are greatly affected by the interaction between river discharge, ocean tides and storm surges. The effects of storm surges on fluvial flooding are further examined from a regional perspective, focusing on the case of Cyclone Sidr in the Ganges‐Brahmaputra‐Meghna Delta in 2007. Modeled results demonstrate that a &gt;3 m storm surge propagated more than 200 km inland along rivers. We show that the performance of global river routing models can be improved by including sea level dynamics.

&amp;lt;p&amp;gt;Coastal flooding is driven by strong winds and low pressures in tropical and extratropical cyclones that generate a storm surge, and high tides. The combination of storm surge and the astronomical tide is defined as the storm tide. Currently over 600 million people live in coastal areas below 10 m elevation worldwide which is projected to increase to more than 1 billion people by 2050 under all Shared Economic Pathways. Towards the end of the 21&amp;lt;sup&amp;gt;st&amp;lt;/sup&amp;gt; century these growing coastal populations will be increasingly at risk of flooding due to SLR. To gain understanding into the threat imposed by coastal flooding and identify areas that are especially at risk, now and in the future, it is crucial to accurately model coastal inundation and assess the coastal flood hazard.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;There are three main types of inundation models with complexity levels ranging from simple, to semi-advanced to advanced. Models capable of simulating inundation at the global scale follow a simple static approach. These models, often referred to as bathtub models, delineate the inundation zone by raising maximum water levels, that correspond to a return period, on a coastal DEM and select all areas that are below the specified water level height. The main limitations of this type of model is that they implicitly assume an infinite flood duration and do not capture relevant physical processes. Regional comparisons have shown that dynamic inundation models are much more accurate than static models in terms of flood extent and depth, and they can provide information on the flood duration.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study we develop a global dataset of storm tide hydrographs. These hydrographs represent the typical shape of an extreme sea level event at a certain location along the global coastline and can be used as boundary conditions for dynamic inundation models. This way we can move away from static to more advanced dynamic inundation models. To assess how different assumptions used for generating hydrographs influence the inundation extent and depth we perform a sensitivity analysis for several coastal regions.&amp;lt;/p&amp;gt;

Multivariate indicator-based flood hazard mapping using primary drivers of coastal flood for India.

CROWELL, M.; COULTON, K.; JOHNSON, C.; WESTCOTT, J.; BELLOMO, D.; EDELMAN, S., and HIRSCH, E., 2010. An estimate of the U.S. population living in 100-year coastal flood hazard areas. Journal of Coastal Research, 26(2), 201– 211. West Palm Beach (Florida), ISSN 0749-0208. The Federal Emergency Management Agency (FEMA) recently completed a coastal demographics study of the United States and U.S. territories. As part of this study, FEMA estimated the United States population subject to the 1% annual chance (100 y) coastal flood hazard as mapped by FEMA. This determination followed a three-step process: (1) create a national digital flood hazard database by compiling the best available coastal-proximate, digital flood-hazard-area data using FEMA data sets; (2) develop a systematic method to separate coastal and riverine flood hazard areas and incorporate this boundary into the digital flood hazard database; and (3) combine the year 2000 census data with the digital flood hazard database using a geographic information system. This enabled estimates of the U.S. population subject to the 1% annual chance coastal flood. The analysis was conducted at the census block-group level, with census block-group populations (permanent residents) assumed to be uniformly distributed across each block group. The results demonstrate that approximately 3.0% of the U.S. population lives in areas subject to the 1% annual chance coastal flood hazard. It must be emphasized, however, that these numbers are based on the 1% annual chance (100 y) coastal flood. Historical coastal floods less frequent than the 1% chance annual flood have occurred in the U.S. on numerous occasions. If less-frequent coastal flood events were considered in this study, such as the 0.2% annual chance (500 y) coastal flood or, if seasonal (vacations) population were considered, then a much greater percentage of the U.S. population would be determined as subject to coastal flooding.