Journal of Flood Risk Management最新文献

The primary focus of the Operational Programme Quality of Environment (OP QE) is the support of EU regions in terms of environmental protection, efficient use of natural resources, flood protection and adaptation to climate change, as well as support of a low-carbon economy. The aim of this article is a spatial analysis of the distribution of financial resources from the OP QE (priority axes 2 and 3) in 2017–2022 at different spatial-hierarchical levels (region, district, and municipality) of Slovakia. The results indicated that there were significant differences in the number of submitted as well as approved/declined projects among individual regions, districts, or municipalities. Regarding the district level, several districts did not submit any project during the studied period, despite the existing flood risk. At the municipal level, out of total municipalities that received funding, approximately half were marked as potentially significantly endangered and approximately another half were marked as not endangered by the Preliminary Flood Risk Assessment—PFRA (2018). Based on this, inclusion of municipalities in different flood risk categories defined by the PFRA (2018) is not being taken into account when approving/declining projects and the same applies to the occurrence of previous flood events in municipalities.

In this paper, a procedure for evaluating and comparing the efficiency between hard and soft flood risk mitigation measures in a highly urbanized area is presented. The European Directive 2007/60/EC promotes, in fact, the implementation of soft measures, which reduce the vulnerability of elements in exposed areas as a risk mitigation strategy and may be less expensive than classic defense measures and sometimes even more effective, but this aspect deserves further investigation. For these reasons, the effectiveness of these hard and soft measures has been here investigated in terms of average annual reduction in expected damage and subsequently through the application of a cost–benefit analysis. The effectiveness of these measures was calculated on a microscale by considering the lifetime of each measure. On the other hand, the efficiency comparison between the different scenarios was carried out at the meso scale, calculating the overall damage reduction for each scenario as the sum of the small contributions of the microscale analysis. The methodology was applied to the case study of the city of Barcellona located within the Longano catchment in the northeastern part of Sicily, Italy. Results showed how the use of soft measures can offer greater efficiency than the use of classical hard defense measures, both in terms of damage reduction and in terms of less time to return from the initial investment.

Natural flood management strategies (NFMs) encompass a variety of measures implemented across catchments to mitigate flood risks while providing multiple benefits. In recent years, NFMs have gained increasing attention from researchers and policymakers. However, despite the growing body of research, there remains a lack of a critical review that quantitatively synthesises the reported performance of different NFMs by analysing their effects on key hydrological parameters. To address this gap, we conducted a systematic review of NFMs based on 145 peer-reviewed papers covering 216 case studies across 37 countries, following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Our analysis moves from a descriptive overview of the evidence base to a novel, quantitative investigation of three critical themes: the characteristics of studied NFM schemes, the methodologies used for their assessment, and their quantitative hydrological performance and its influencing factors. Results indicate that 31% of the studies identified flood peak reduction as the most commonly targeted hydrological objective. A significant positive correlation was found between intervention diversity and intensity (Spearman's ρ = 0.53). Furthermore, our methodological analysis reveals a critical trade-off in the literature, with empirical monitoring typically used in small catchments over shorter durations, while modelling is used to assess a greater diversity of interventions at larger scales, with truly combined approaches being notably rare (11%). Notably, river and floodplain management (RFM) demonstrated higher effectiveness, achieving an average flood peak reduction of 30%, particularly in larger catchments. Bearing the often multi-faceted aims of NFMs in mind, this paper provides key suggestions for future research.

Rapid urban flood mapping is crucial for timely risk alerts and emergency relief. Machine learning (ML)-based mapping models emerge as a promising approach for fast, accurate inundation forecasts. However, current ML models often use precipitation features as inputs and predict maximum flood depth for all grid cells of a specific region simultaneously. This special design improves their prediction efficiency but limits their application in new regions. This study aims to create a highly adaptable, rapid urban maximum flood water depth mapping model based on the random forest regression algorithm and the extreme gradient boosting algorithm. Our mapping model additionally incorporates terrain and land-use features, besides the precipitation feature, as input variables and generates the maximum water depth only for a grid cell in each mapping. Thus, it can be unchangeably applied to the grid cells in a new area when the model is fully trained. In the case study of Shenzhen, China, our ML-based mapping model demonstrated excellent mapping ability in both training and validation sets. The coefficient of determination (R²) is consistently greater than or close to 95%. Furthermore, it revealed good generalization ability when directly applied to a new rainfall event (R² = 0.875) and a new area (R² = 0.810). Meanwhile, the time cost of the mapping model is less than 3 s, meeting the requirement for real-time mapping. These results indicate that this highly adaptable model, once appropriately trained, can be applied to rapid urban flood severity mapping, which significantly reduces its use cost in urban flood management.

Temporary structures are important for a rapid recovery phase after extraordinary flood disasters we cannot protect ourselves from. Long-term temporary structures are particularly relevant when infrastructures are destroyed that require a longer reconstruction phase. In addition, they offer the opportunity of more time to build resilient critical infrastructure (CI). The term “long-term temporary” is used in the study to emphasize that these temporary solutions are not only used for a short period of time (less than 6 months). On the example of the recovery in the Ahr valley after the 2021 flood, the authors diagnosed the importance of practice examples on long-term temporaries when ad-hoc solutions are needed, as well as the long persistence of some of the temporary solutions. A systematic literature analysis was conducted, as limited research in long-term temporaries exists. We evaluated how many scientific papers on the topic of long-term temporaries for CI after flood disasters can be identified after a parameter-oriented literature analysis and which aspects are dealt with. The literature analysis is based on seven search parameter combinations and covers the areas of drinking water supply, power supply, sewage disposal, telecommunications, bridges (transport systems) and gas supply. 138 publications were identified as relevant, with 43 broaching the issue of temporary solutions after flooding. The most common keyword is “critical infrastructure” (CI) with only 3.7%, followed by “flood” with only 3.4%. Most studies on temporary solutions evaluate temporary bridges, followed by drinking water supply. Military engineering plays a key role in providing temporary bridges, which explains the good supply and documentation. The authors analysed temporary structural solutions (long-term temporaries) based on on-site observations and the close collaboration with municipalities within the KAHR-project during the recovery phase of the region. The case study presents some specific long-term temporary solutions for bridge constructions and flying pipes to temporary drinking water treatment systems and sewage treatment plants. Another key finding is that long-term temporary structures are very diverse and have varying life spans (shorter for telecommunication and drinking water supply and longer for bridges and sewage disposal) as well as different requirements in technicality and durability (e.g., lower challenges in drinking water supply, higher requirements for bridges). It is therefore important to explore this area in terms of risks and design options, which has a direct impact on flood risk management, as it could make the use of long-term temporary structures more routine during the emergency management phase.

Urban flooding is a growing challenge in metropolitan areas, exacerbated by climate change and increasing urbanization. This study develops an innovative flood warning system for the Guarará Basin in Santo André, Brazil, leveraging both parametric and machine learning (ML) models. Rainfall data from the São Paulo State Flooding Alert System and historical flood records were processed using the dynamic Thiessen polygon method and advanced statistical techniques. A parametric model was calibrated to define alert thresholds, while a Random Forest (RF) classifier was trained to predict five alert levels: “No Rain,” “Raining,” “Vigilance,” “Warning,” and “Alert”. The models were validated against historical events from 2016 and 2019, demonstrating strong agreement in predicting alert levels and highlighting the benefits of combining physical interpretability with data-driven adaptability. The ML model achieved an overall weighted F1-score of 0.99, showcasing its effectiveness in classifying rainfall events and issuing timely warnings. This integrated methodology offers a robust framework for flood risk management in urban areas, contributing to the development of sustainable and resilient cities.

Flood risk assessment serves as a critical tool, providing theoretical foundations for minimizing flood damage and effective flood management. In June 2023, the Kakhovka Dam failure highlighted the need for accurate flood risk assessment under different breach scenarios. This study assessed the spatiotemporal flood risk to population and built-up areas under partial (S1) and complete (S2) dam-breach scenarios using the HEV (hazard–exposure–vulnerability) framework. Hotspot analysis was used to identify high-risk zones, and classification differences between HEV and DV methods were compared. The results are as follows: Flood propagation was simulated using the TELEMAC-2D model, yielding an NSE of 0.98 based on observed water depths. Spatial validation produced a precision (P) of 70.7%, a false positive rate (FPR) of 22.9%, a false negative rate (FNR) of 3.2%, and an accuracy of 84.6%. For population flood risk, the total number of people at risk was approximately 101,000 under S1, increasing sharply within the first 41 h. Under S2, the total rose to approximately 296,000, with a rapid increase observed in the first 20 h. For built-up area, the total flood-affected extent was 51.6 km² under S1, showing a sharp increase in the first 41 h. Under S2, the total affected area expanded to 157.4 km², with a rapid rise during the first 20 h. High-risk clusters of both population and buildings were mainly located near the dam site and in the midstream right-bank urban area. New high-risk zones also emerged in the southern part of the left riverbank under S2. Although the DV-based method produced a wider spatial extent of flood risk, it underestimated risk levels in both scenarios. The results provide a practical basis for flood risk assessment and emergency management.

In summer 2021, heavy precipitation caused major flooding in central Europe, affecting areas in Germany, the Netherlands, and Belgium. The Ahr Valley in Germany was one of the most adversely affected areas, with more than 135 deaths and major destruction within a 50 km path along the Ahr. The federal government of Germany and the federal states affected established a reconstruction fund of 30 billion euros. The recovery and reconstruction process is still ongoing. Much attention has been given to the analysis of the flood disaster; however, this paper explores and documents how selected scientific recommendations developed within a transdisciplinary project (called KAHR) have influenced decisions within the reconstruction process in terms of strengthening climate-resilient recovery. We assess factors that increased the uptake and impact of selected scientific recommendations as well as factors that hindered the uptake. We find, for example, that the urgency for rebuilding large parts of the Ahr Valley and the fact that policy processes were open for scientific inputs increased the uptake and impact. Also, the transdisciplinary nature of the KAHR project helped in translating science into practice. In contrast, time pressure to reconstruct rapidly, uncertainties of what is going to be financed by the reconstruction fund, and existing zoning and building regulations hindered the uptake of selected scientific recommendations toward resilience building. Finally, we argue that science needs a formal role in post-disaster reconstruction processes in order to strengthen resilience, as this allows the latest scientific findings to be incorporated to support resilient reconstruction and allows for a more neutral perspective in discussions and decisions.

Flash floods cause high numbers of casualties and enormous economic damage. Good knowledge of the damage processes is crucial for the implementation of effective flash flood risk management. However, little is known about the damage processes that occur during flash floods, despite their severity. To gain more knowledge, independent data collection initiatives were carried out in the affected areas of Belgium and Germany after the 2021 floods. The resulting datasets include 420 damaged residential buildings in the Vesdre valley in Belgium, 277 in the Ahr valley in Rhineland-Palatinate (Germany) and 332 in North Rhine-Westphalia (Germany). A total of 30 potential damage-influencing variables were harmonized across the regions, providing valuable insights into hazard characteristics, the vulnerability of exposed assets, the coping capacity of inhabitants, and socio-economic factors. Machine learning-based analysis reveals the significant importance of hazard variables, such as water depth and sediment transport, particularly for building damage. In addition to these, exposure (living area) and physical vulnerability factors (building type and wall type) also play a role in determining building damage across the affected regions. For content damage, besides water depth and living area, socio-economic vulnerability (ownership status of the building) and emergency measures were found to be important predictors. These key drivers of building and content damage from flash floods can be utilized to develop more accurate damage models, thereby improving flash flood risk assessments, enhancing risk communication, and supporting better preparedness strategies.

Global climate change exacerbates urban floods, making their projection into future uncertainties more challenging. Identifying flood impact factors in urban areas is necessary for effective urban flood risk management. However, studies investigating the priority determination among flood impact factors based on an integrated decision-making tool are limited. This study proposes an integrated flood risk matrix combining two methods. The proposed tool comprises quantitative and qualitative approaches to comprehensively investigate the priorities among flood impact factors. The quantitative approach examines the “uncertainty,” and the qualitative approach investigates the “importance”. The proposed tool, combined with two measures, performs priority determination with respect to hydrological and hydraulic flood risk factors. Pipe roughness and curve number were identified as the key drivers (i.e., high priority). In addition, the proposed matrix demonstrated how priority determination among flood impact factors can help improve decision-making for urban infrastructure projects. This study improves knowledge of project decision-making by providing a mechanism that integrates two different methods while providing reliable results.