Risk-based approaches have been increasingly accepted and operationalized in flood risk management during recent decades. For instance, commercial flood risk models are used by the insurance industry ...to assess potential losses, establish the pricing of policies and determine reinsurance needs. Despite considerable progress in the development of loss estimation tools since the 1980s, loss estimates still reflect high uncertainties and disparities that often lead to questioning their quality. This requires an assessment of the validity and robustness of loss models as it affects prioritization and investment decision in flood risk management as well as regulatory requirements and business decisions in the insurance industry. Hence, more effort is needed to quantify uncertainties and undertake validations. Due to a lack of detailed and reliable flood loss data, first order validations are difficult to accomplish, so that model comparisons in terms of benchmarking are essential. It is checked if the models are informed by existing data and knowledge and if the assumptions made in the models are aligned with the existing knowledge. When this alignment is confirmed through validation or benchmarking exercises, the user gains confidence in the models. Before these benchmarking exercises are feasible, however, a cohesive survey of existing knowledge needs to be undertaken. With that aim, this work presents a review of flood loss-or flood vulnerability-relationships collected from the public domain and some professional sources. Our survey analyses 61 sources consisting of publications or software packages, of which 47 are reviewed in detail. This exercise results in probably the most complete review of flood loss models to date containing nearly a thousand vulnerability functions. These functions are highly heterogeneous and only about half of the loss models are found to be accompanied by explicit validation at the time of their proposal. This paper exemplarily presents an approach for a quantitative comparison of disparate models via the reduction to the joint input variables of all models. Harmonization of models for benchmarking and comparison requires profound insight into the model structures, mechanisms and underlying assumptions. Possibilities and challenges are discussed that exist in model harmonization and the application of the inventory in a benchmarking framework.
In this paper, the catastrophic flash flood event which occurred in the western part of Attica (Greece) in November 2017 is reconstructed. The flood event hit the town of Mandra, causing 24 ...fatalities and huge damages in the properties and the infrastructure. The flood hydrograph was derived using the two-dimensional hydrodynamic model FLOW-R2D. Attention was drawn on the uncertainties of the model output due to the uncertainty of the estimated parameters such as infiltration, friction and the uncertainty of input data. Due to the computational burden related to the model, a global sensitivity analysis based on Morris method was performed. Then, a Monte Carlo-based uncertainty analysis was performed on the two most influential factors. It was concluded that even the results of the physically based hydrodynamic models are characterised by uncertainties. However, the capability of the hydrodynamic models to describe in detail the dynamics of the overland flow is the main advantage of these models against the conventional hydrological models. It is concluded that the rational use of physically based models for analysing complex storm phenomena with high variable spatial and temporal distribution can lead to a more accurate range of magnitudes of flood peak.
Flood hazards are common in Bhutan as a result of torrential rainfall. Historical flooding events also point to flooding during the main monsoon season of the year, which has had a huge impact in ...many parts of the country. To account for climate change patterns in flood hazards in Bhutan, 116 historical flood events between 1968 and 2020 for 20 districts were retrieved and reviewed. The preliminary review revealed that the frequency of flood occurrence has increased by three times in recent years. In this study, seven flood vulnerability (FV) indicators were considered. Five are the attributes of historical floods, classified into a number of incidents for flood events, fatalities, affected population, and infrastructure damages including economic losses. Additionally, the highest annual rainfall and existence of a flood map were other two indicators considered. Using historical data, flood hazard and impact zonation were performed. The analytic hierarchy process (AHP) method was employed to derive a multi-criteria decision model. This resulted in priority ranking of the seven FV indicators, broadly classified into social, physical/economic, and environmental. Thereafter, an indicator-based weighted method was used to develop the district flood vulnerability index (DFVI) map of Bhutan. The DFVI map should help researchers understand the flood vulnerability scenarios in Bhutan and use these to mediate flood hazard and risk management. According to the study, FVI is very high in Chhukha, Punakha, Sarpang, and Trashigang districts, and the index ranges between 0.75 to 1.0.
Mitigating the adverse impacts caused by increasing flood risks in urban coastal communities requires effective flood prediction for prompt action. Typically, physics‐based 1‐D pipe/2‐D overland flow ...models are used to simulate urban pluvial flooding. Because these models require significant computational resources and have long run times, they are often unsuitable for real‐time flood prediction at a street scale. This study explores the potential of a machine learning method, Random Forest (RF), to serve as a surrogate model for urban flood predictions. The surrogate model was trained to relate topographic and environmental features to hourly water depths simulated by a high‐resolution 1‐D/2‐D physics‐based model at 16,914 road segments in the coastal city of Norfolk, Virginia, USA. Two training scenarios for the RF model were explored: (i) training on only the most flood‐prone street segments in the study area and (ii) training on all 16,914 street segments in the study area. The RF model yielded high predictive skill, especially for the scenario when the model was trained on only the most flood‐prone streets. The results also showed that the surrogate model reduced the computational run time of the physics‐based model by a factor of 3,000, making real‐time decision support more feasible compared to using the full physics‐based model. We concluded that machine learning surrogate models strategically trained on high‐resolution and high‐fidelity physics‐based models have the potential to significantly advance the ability to support decision making in real‐time flood management within urban communities.
Key Points
Surrogate machine learning models were trained for flood prediction using a high‐resolution and high‐fidelity physics‐based model
The surrogate model accurately emulated flooding depth and duration on streets simulated by the physics‐based model
A 3,000 times speedup was achieved with the surrogate model compared to the physics‐based model, making it attractive for real‐time decision support
Flood mitigation involves the management and control of floodwater movement, such as redirecting flood runoff through the use of floodwalls and flood gates, rather than trying to prevent floods ...altogether. The prevention and mitigation of flooding can be studied on three levels: on individual properties, small communities, and whole towns or cities. The current study area is located in Hurghada on the Red Sea, which is considered an important area for coastal tourism. The study area is located at distance 7.50 km from El Gouna city along the Red Sea and east of Hurghada–Al Ismaileya road. The aim of this research is to derive the runoff flow paths across the study area and their flow magnitudes under different rainfall events of 10, 25, 50, and 100 year return periods in order to design the flood mitigation measures to protect such important areas. Field data (e.g., topographic data and rainfall intensities) were collected for the study area. The results indicated that the site is exposed to high flash flood risk and protection work is required. In order to protect the area from flood risks, locations of number of drainage channels and dams were selected and designed based on flood quantity and direction. The proposed mitigation system is capable of protecting this crucial area from flood risks and increases the national income from tourism. This study can be applied in different areas of Egypt and the world.
Due to the severity related to extreme flood events, recent efforts have focused on the development of reliable methods for design flood estimation. Historical streamflow series correspond to the ...most reliable information source for such estimation; however, they have temporal and spatial limitations that may be minimized by means of regional flood frequency analysis (RFFA). Several studies have emphasized that the identification of hydrologically homogeneous regions is the most important and challenging step in an RFFA. This study aims to identify state‐of‐the‐art clustering techniques (e.g., K‐means, partition around medoids, fuzzy C‐means, K‐harmonic means, and genetic K‐means) with potential to form hydrologically homogeneous regions for flood regionalization in Southern Brazil. The applicability of some probability density function, such as generalized extreme value, generalized logistic, generalized normal, and Pearson type 3, was evaluated based on the regions formed. Among all the 15 possible combinations of the aforementioned clustering techniques and the Euclidian, Mahalanobis, and Manhattan distance measures, the five best were selected. Several watersheds' physiographic and climatological attributes were chosen to derive multiple regression equations for all the combinations. The accuracy of the equations was quantified with respect to adjusted coefficient of determination, root mean square error, and Nash–Sutcliffe coefficient, whereas, a cross‐validation procedure was applied to check their reliability. It was concluded that reliable results were obtained when using robust clustering techniques based on fuzzy logic (e.g., K‐harmonic means), which have not been commonly used in RFFA. Furthermore, the probability density functions were capable of representing the regional annual maximum streamflows. Drainage area, main river length, and mean altitude of the watershed were the most recurrent attributes for modelling of mean annual maximum streamflow. Finally, an integration of all the five best combinations stands out as a robust, reliable, and simple tool for estimation of design floods.
Short‐term weather forecasts have the potential to improve reservoir operations for both flood control and water supply objectives, especially in regions currently relying on fixed seasonal flood ...pools to mitigate risk. The successful development of forecast‐based policies should integrate uncertainty from modern forecast products to create unambiguous rules that can be tested on out‐of‐sample periods. This study investigates the potential for such operating policies to improve water supply efficiency while maintaining flood protection, combining state‐of‐the‐art weather hindcasts with downstream conjunctive use to transfer surplus flood releases to groundwater storage. Because available weather hindcasts are relatively short (10–20 years), we propose a novel statistical framework to develop synthetic forecasts over longer periods of the historical record. Operating rules are trained with a recently developed policy search framework in which decision rules are structured as binary trees. Policies are developed for a range of scenarios with varying forecast skill and conjunctive use capacity, using Folsom Reservoir, California, as a case study. Results suggest that the combination of conjunctive use and short‐term weather forecasts can substantially improve both water supply and flood control objectives by allowing storage to remain high until forecasts trigger a release. Further, increased conjunctive use capacity reduces the importance of forecast skill, since surface storage can be moved to groundwater during the flood season without losing water supply. This analysis serves the development of forecast‐based operating policies for large, multipurpose reservoirs in California and other regions with similar flood hydroclimatology.
Key Points
Short‐term forecasts can help balance water supply and flood control objectives in areas dominated by synoptic‐scale flood hydroclimatology
The combined use of forecasts and conjunctive use provides complementary benefits for water supply/flood control tradeoffs
Forecast uncertainty of state‐of‐the‐art numerical weather prediction models does not preclude forecast use in reservoir management
Los Angeles (LA) County's coastal areas are highly valued for their natural benefits and their economic contributions to the region. While LA County already has a high level of exposure to flooding ...(e.g. people, ports, and harbors), climate change and sea level rise will increase flood risk; anticipating this risk requires adaptation planning to mitigate social, economic, and physical damage. This study provides an overview of the potential effects of sea level rise on coastal LA County and describes adaptation pathways and estimates associated costs in order to cope with sea level rise. An adaptation pathway in this study is defined as the collection of measures (e.g., beach nourishment, dune restoration, flood‐proofing buildings, and levees) required to lower flood risk. The aim of using different adaptation pathways is to enable a transition from one methodology to another over time. These pathways address uncertainty in future projections, allowing for flexibility among policies and potentially spreading the costs over time. Maintaining beaches, dunes, and their natural dynamics is the foundation of each of the three adaptation pathways, which address the importance of beaches for recreation, environmental value, and flood protection. In some scenarios, owing to high projections of sea level rise, additional technical engineering options such as levees and sluices may be needed to reduce flood risk. The research suggests three adaptation pathways, anticipating a +1 ft (0.3 m) to +7 ft (+2 m) sea level rise by year 2100. Total adaptation costs vary between $4.3 and $6.4 bn, depending on measures included in the adaptation pathway.
About 10% of Europe's surface area is prone to rapid flooding of rivers confined in valleys. The devastating potential of such floods is exacerbated by the deficits of existing gauging networks, ...including low station densities and recording frequencies, and lack of information beyond stage height. Here, we use seismic data of the July 2021 Ahrtal flood, Germany, to extract information to complement sparse hydrometric data, and to reconstruct the rapid evolution of this fatal event. We show that a seismic station can deliver essential flood metrics such as magnitude, propagation velocity and debris transport rate. These seismic products provide high resolution insight to the non‐linear flood behavior. We argue that an approach combining distributed low‐cost seismometers with existing seismic stations, can provide important real time data on future catastrophic floods and associated hazards in upland catchments, offering precious response time also in currently ungauged landscapes.
Plain Language Summary
Rapidly evolving floods are a major hazard for 10% of European landscapes. They are hard to adequately detect and describe by the classic gauge station scheme, but seismic sensors provide a valuable alternative to this difficulty. A seismometer can sense a flood like the devastating one that hit the Ahr valley, Germany, in July 2021 up to 1.5 km away. The seismic footprint of the flood allows to provide information on flood magnitude, velocity and trajectory at sub‐minute resolution and at near real time. We show how this new approach can be utilized for future flood protection.
Key Points
Seismic sensing of valley confined floods improves classic detection approaches
Near‐real time information on flood magnitude, trajectory, and velocity
Gate keeper seismometer networks can improve flood risk management in Europe
State‐of‐the‐art flood hazard maps in coastal cities are often obtained from simulating coastal or pluvial events separately. This method does not account for the seasonality of flood drivers and ...their mutual dependence. In this article, we include the impact of these two factors in a computationally efficient probabilistic framework for flood risk calculation, using Ho Chi Minh City (HCMC) as a case study. HCMC can be flooded subannually by high tide, rainfall, and storm surge events or a combination thereof during the monsoon or tropical cyclones. Using long gauge observations, we stochastically model 10,000 years of rainfall and sea level events based on their monthly distributions, dependence structure and cooccurrence rate. The impact from each stochastic event is then obtained from a damage function built from selected rainfall and sea level combinations, leading to an expected annual damage (EAD) of $1.02 B (95th annual damage percentile of $2.15 B). We find no dependence for most months and large differences in expected damage across months ($36–166 M) driven by the seasonality of rainfall and sea levels. Excluding monthly variability leads to a serious underestimation of the EAD by 72–83%. This is because high‐probability flood events, which can happen multiple times during the year and are properly captured by our framework, contribute the most to the EAD. This application illustrates the potential of our framework and advocates for the inclusion of flood drivers' dynamics in coastal risk assessments.
Plain Language Summary
In coastal cities, floods can result from different drivers such as intense rainfall and extreme sea levels. In order to estimate the expected annual damage (EAD) from flooding, it is important to correctly quantify the chance of these events happening and their impacts. Commonly, only the maximum value for a flood driver each year is taken, ignoring its seasonality; and these maxima are assumed to either never or always happen at the same time of the year. These assumptions become problematic when pluvial and coastal floods can occur at different times of the year and sometimes at the same time, such as in Ho Chi Minh City (HCMC). In this paper, we develop a framework to account for the seasonality of flood drivers and their mutual dependencies. Using statistical and hydrodynamic modeling, we generate the equivalent of 10,000 years of rainfall and sea level events and estimate their impact. We find the EAD for HCMC to be $1.02 B. Neglecting the seasonality of rainfall and sea level leads to an underestimation of the EAD by 72–83%. This application illustrates the potential of our framework and advocates for the inclusion of flood drivers' dynamics in coastal risk assessments.
Key Points
We develop a flood risk framework based on monthly time series that includes the seasonality and the dependence between flood drivers
Not accounting for seasonality underestimates the expected annual damage (EAD) in Ho Chi Minh City by 72–83%
Rainfall and storm surge are independent for most months but, under the assumption of full correlation, EAD would increase by 15%
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