Journal of Flood Risk Management最新文献

Levees play a crucial role in flood protection, but globally, there is a need for more knowledge about levee networks and their flood routing effects. Without complete knowledge, the question arises: ‘What is the flood risk associated with an unknown or partially known levee portfolio?’ Unknown or undocumented levees can be maladaptive and undermine system resilience. However, current literature often does not acknowledge undocumented levees, assuming all assets are known. A greater understanding would provide insight into present vulnerabilities and enable more complete management of our flood protection systems, reducing communities' risk. Our research assessed the physical condition of two undocumented levees in a case study. Computational flood modelling then simulated (1) their present condition, (2) their removal and (3) their reconstruction to a good physical condition. This determined their effect on inundation area and building damages, allowing their classification. The undocumented levees in the case study were significantly degraded, leading to an insignificant impact on flood routing and flood damages in their present state. However, if reconstructed, the levees could be valuable if the surrounding land were developed. More broadly, this study illustrates the importance of identifying and integrating undocumented levees into network modelling and maintenance.

Devastating flood events are recurrently impacting West Africa. To mitigate flood impacts and reduce the vulnerability of populations, a better knowledge on the frequency of these events is crucial. The lack of reliable hydrometric datasets has hitherto been a major limitation in flood frequency analysis at the scale of West Africa. Utilising a recently developed African database, we perform a flood frequency analysis on the annual maximum flow (AMF) time series, covering 246 river basins in West Africa, between 1975 and 2018. Generalized extreme value (GEV) and Gumbel probability distributions were compared to fit AMF time series with the L-moments, Maximum Likelihood (MLE) and Generalized Maximum Likelihood (GMLE) methods. Results indicated that the GEV distribution with the GMLE method provided the best results. Regional envelope curves covering the entire West African region with unprecedented data coverage have been generated for the first-time providing insights for the estimation in flood quantiles for ungauged basins. The correlation between flood quantiles and watershed properties shows significant correlations with catchment area, groundwater storage, altitude and topographic wetness index. The findings from this study are useful for a better flood risk assessment and the design of hydraulic infrastructures in this region, and are a first step prior to the development of regional approaches to transfer the information from gauged sites to ungauged catchments.

Flood hazard assessment is crucial to mitigate the risks associated with flooding. Integrating levee failure scenarios into these assessments should improve the evaluation of flood risks and enhance the resilience of communities and infrastructure. This research presents a probabilistic flood hazard approach to assess levee failure and its impact on flood hazard. Our method includes a comprehensive assessment of backward erosion and overflowing failure mechanisms, integrated within a 1D/2D hydraulic model that simulates flood propagation and levee breaching. We calculate the cumulative probability of flood depth and velocity considering various scenarios, taking into account levee failure breaching for various failure mechanisms and several flood intensities. We apply the method to a residential area along Etobicoke Creek in Ontario, Canada. The results highlight which levee segment has the most impact on flood hazard, emphasizing the importance of incorporating levee failure scenarios in flood hazard assessments. The cumulative probability curve provides a more holistic result in locating the most hazardous areas rather than considering one return period or one failure mechanism. It can be expended to every location of the protected area, allowing for the creation of a probabilistic map for a desired probability.

The current practice of flood loss prediction presents limitations in accurately predicting building flood losses at multiple scales. While whole-building estimates can more accurately predict high-level losses (i.e., large groups of buildings), a significant analysis error is revealed with small-scale (i.e., individual, or small groups of buildings) investigation. This research presents a more robust, data driven, small-scale, flood damage estimation approach for residential buildings. The approach is based on component-level, depth–damage curves derived from experimental analysis. Structures with standard residential construction materials typical to the south-eastern United States were built and incrementally flooded for short durations. The materials were assessed to determine the level of damage inflicted. This experimentally derived damage data were then translated into a set of flood depth–damage functions (DDFs). The DDFs were tailored for analysis at smaller scales and incorporated the ability to apply damage uncertainty in damage analysis. To demonstrate the applicability of the experimentally derived DDFs to damage estimation at smaller scales, the functions are applied to a hypothetical building design typical of the south-eastern United States.

The damage potential in river systems due to flood flows has increased as a result of the increased infrastructure and population growth along river corridors, frequently accompanied by an incomplete understanding of flood safety and the impact of climate change. In particular, so-called catastrophic flood events in alpine areas are generally accompanied by massive channel beds and floodplain erosion with a higher damage potential than inundation only. It has been recently shown that critical flow conditions might be an important driver of uncontrolled erosion and channel avulsion in terms of extraordinary flood events. A systematic analysis, however, of the parameters driving critical flow conditions is lacking. Thus, the aim of this study was to conduct a systematic numerical evaluation of the hydraulic parameters responsible for critical flows in steep mountain channels as a baseline study for future improved flood impact assessment and mitigation measure design. The systematic analysis of standardized river bathymetries revealed that channel slope, roughness and river widening impose decreasing influences on alpine rivers to produce critical flow conditions. However, there is a risk that due to human interventions, altering the natural slope–roughness relationship to increase the discharge capacity for flood safety might promote critical conditions. These findings should be considered in future hydraulic engineering practice.

Flood risk models provide important information for disaster planning through estimating flood damage to exposed assets, such as houses. At large scales, computational constraints or data coarseness often lead modelers to aggregate asset data using a single statistic (e.g., the mean) prior to applying non-linear damage functions. This practice of aggregating inputs to nonlinear functions introduces error and is known as Jensen's inequality; however, the impact of this practice on flood risk models has so far not been investigated. With a Germany-wide approach, we isolate and compute the error resulting from aggregating four typical concave damage functions under 12 scenarios for flood magnitude and aggregation size. In line with Jensen's 1906 proof, all scenarios result in an overestimate, with the most extreme scenario of a 1 km aggregation for the 500-year flood risk map yielding a country-wide average bias of 1.19. Further, we show this bias varies across regions, with one region yielding a bias of 1.58 for this scenario. This work applies Jensen's 1906 proof in a new context to demonstrate that all flood damage models with concave functions will introduce a positive bias when aggregating and that this bias can be significant.

Hazard vulnerability assessment of critical infrastructures (CIs) is crucial for ranking infrastructures based on their level of criticality, enabling the urban managers to prioritize CIs for allocating funds in the hazard mitigation/recovery process. This study aims to provide a framework for ranking CIs based on a rapid and preliminary flood vulnerability assessment by introducing a methodology for classifying CIs according to their vulnerability to riverine flooding. An indicator-based vulnerability curve is calculated both quantitatively (using Fuzzy Logic Toolbox in MATLAB) and qualitatively (using susceptibility–exposure matrix), based on which CIs prioritization is accomplished with a focus on functional flood vulnerability considering structural/nonstructural damages. Besides, this study addresses the consequences that a damaged infrastructure may have on the rest of CIs and estimates their vulnerability given the additive impact of the surrounding failed infrastructures considering their interdependence. The methodology was applied to Berat (Albania) and Sarajevo (Bosnia-Herzegovina) with findings compared to those of a multi-criteria decision-making-based approach commonly used in CI ranking literature. The obtained results from both methods represent that roads are the most vulnerable studied infrastructure in the case of Berat, while regarding the city of Sarajevo, road infrastructures are considered the least vulnerable to riverine floods compared to bridges and schools.

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