Journal of Flood Risk Management最新文献

Across the world, communities face annual and increasingly extreme flood events, yet there is a widespread lack of proactive preparedness. This failure to anticipate and mitigate flood risks deepens the damages experienced, stalling development, undermining environmental sustainability, and driving many communities deeper into poverty. Anticipatory action has emerged as a proactive strategy in river flood risk management, aiming to reduce vulnerabilities and enhance community resilience before disasters strike. This study assesses the implementation of anticipatory action strategies in Nigeria by building on qualitative data to assess community vulnerabilities and capacities. Findings indicate that over 70% of the total number of respondents in the selected nine communities in Nigeria lacked access to timely early warnings, and more than half viewed floods as unavoidable, reducing their engagement in long-term resilience planning. Communities demonstrated a stronger preference for short-term relief over proactive preparedness for disasters. Findings reveal a convergence of structural and behavioral vulnerabilities within the population. This highlights the study's contribution by connecting behavioral insights with anticipatory frameworks in high-risk communities. The study shows that there is a clear need for community-driven approaches that combine anticipatory action with economic support, sustained engagement, and other adaptive measures. By closing both behavioral and structural gaps, more effective anticipatory action policies can be institutionalized.

Mountainous river basins, typically located in river source areas, are characterized by steep terrain and dynamic landforms. These regions experience diverse climates due to topographic uplift, making them susceptible to frequent flash floods. The rapid onset and brief response time of flash floods pose significant challenges for achieving accurate and timely forecasting within limited warning periods. Deep learning models have emerged as powerful tools for high-precision streamflow forecasting. This study develops an LSTM-based multi-sliding window flood forecasting model for various lead times and applies it to the Qinling Mountains watershed, with an emphasis on analyzing the model's interpretability. Results from the Maduwang Basin demonstrate the model's excellent performance in flood prediction for 1- and 3-h lead times. While incorporating historical data can enhance model performance for long lead times, excessive historical inputs may be detrimental. Historical runoff significantly influences model performance. However, its contribution neither consistently increases with temporal proximity to the prediction time nor remains uniformly positive. The contribution of input features varies across different flood stages and can be explained by existing hydrological knowledge. This research demonstrates the potential of deep learning for flood forecasting in mountainous basins while providing insights into the interpretation of deep learning models. This provides scientific support for flood warning systems and emergency management.

Synthetic rating curves (SRCs) are often used to translate streamflow forecasts into flood inundation maps. Previous studies have investigated the development and errors in SRCs at local, regional, and continental scales. In this analysis, we used the latest global methodology and datasets to develop SRCs for use in flood inundation map forecasting. Using the Yellowstone River Basin and the 2022 floods that affected the region, we analyzed the error in the SRCs assessment of stage and water surface elevation (WSE). We then investigated the error in flood inundation maps produced using the SRCs. Comparing SRCs to locally derived rating curves from 29 U.S. Geological Survey (USGS) stream gages, median error in SRC stage ranged from 0.45 to 0.65 m and SRC error was greatest at higher magnitude streamflows. This error increased to a median of 1.98–2.30 m when converting the stage to a WSE. After using the SRC WSE estimates to create an estimated flood inundation map, the WSE error at observed high-water marks (1.99 m) was nearly proportional to average WSE error at the stream gage locations along the same river reach. Our results provide the first regional assessment of globally derived SRCs that are used in flood inundation mapping.

Urban Flood Risk Management (FRM) is a critical aspect of developing resilient environments for future generations to inhabit. It is now interconnected with the requirement to be more environmentally conscious through blue-green infrastructure and the delivery of wider co-benefits. The complexity of balancing urban growth with environmental drivers and increasing resilience is a key challenge for strategic urban decision-making. Through computational modelling developments, new approaches to assess the spatial contribution of area to flood hazard are improving our understanding of the catchment response and our ability to develop multifunctional, multi-beneficial projects. Yet at present, these approaches remain largely theoretical or are a ‘best intention’. This study uses an adapted ‘Unit Flood Response’ approach to generate Flood Source Area (FSA) maps for an urban catchment in the UK. A user-focused engagement approach is applied using FSA outputs to generate key insight into its applicability from a practitioner perspective. The FSA modelling identified several hazard sources, from widespread contributions upstream to discrete contributions downstream. Stakeholders concluded that the FSA can support FRM at the pre-planning stage by providing a clearer strategic vision across the catchment to support traditional ‘receptor-led’ decision-making. Improved identification and negotiation of project partners and the potential to support/identify wider scale options that integrate with existing and planned infrastructure in other sectors, for example, housing and transport, were additional benefits of this approach. While the computational aspects of FSA analyses could be improved for model robustness (e.g., calibration, validation), they must do so with a full understanding of the practicalities of applying these techniques on the ground, demonstrating the importance of co-development of research with practitioners and decision-makers.

Flooding poses a persistent challenge in Bangladesh, where complete prevention remains difficult due to its geographical and climatic conditions. This study integrates the Analytical Hierarchy Process (AHP) with Geographic Information System (GIS) techniques to create a detailed flood susceptibility map for the Sylhet division in northern Bangladesh. The primary goal is to classify the region into distinct flood susceptibility zones, providing valuable insights for improving flood risk management, mitigation, and preparedness strategies. The study evaluates 12 critical flood-influencing parameters, including elevation, slope, topographic wetness index (TWI), precipitation, drainage density, proximity to roads and rivers, vegetation, land use and land cover (LULC), and soil type. These factors were chosen based on their established relevance to flood dynamics, with data sourced from reliable spatial databases to ensure accuracy. Using AHP, weights were assigned to each parameter based on expert input, reflecting their relative importance in flood risk. These weighted factors were then integrated using GIS overlay analysis and weighted linear combination techniques to generate a flood susceptibility map. The results show that approximately 35.27% of the Sylhet division, particularly the northern regions and the low-lying Haor basin, fall into the “high” flood susceptibility categories. These areas are highly vulnerable due to their flat topography, proximity to major rivers, and inadequate drainage systems. In contrast, the southern and southwestern areas, accounting for around 7.45% of the region, exhibit “low” flood susceptibility, benefiting from higher elevations and better natural drainage. This flood susceptibility map serves as an essential tool for identifying high-risk areas, supporting targeted flood mitigation efforts, and enhancing disaster preparedness. By providing a scientific foundation for effective flood management, the study aids decision-makers in reducing flood impacts and promoting the sustainable development of flood-prone regions in northern Bangladesh.

While natural flood management (NFM) as a flood mitigation strategy is becoming widely used, there remains a lack of evidence regarding the effectiveness of different NFM scenarios under high flow events. To demonstrate how different types and extents of NFM interventions interact to flood peaks at larger catchment scales, combined scenarios of existing NFM interventions and an ideal maximum woodland scenario were modelled in the Upper Aire, northern England, using a coupled model that integrates Spatially Distributed TOPMODEL (SD-TOPMODEL) with a 2D hydrodynamic model (Flood Modeller 2D) at an 81.4 km² catchment. The coupled model exhibited a strong fit with observed data (NSE up to 0.95), effectively capturing flood peaks and peak shapes. Leaky dams were found to be more effective at delaying flood peaks with mean values ranging from 8.6 to 60 min than reducing peak discharge (mean values ranging from 0.53% to 1.84%), though these effects were inversely proportional and influenced by tributary characteristics such as channel gradient. Simulations applying multiple NFM interventions consistently demonstrated positive flood mitigation impacts, including reduced peak discharge up to 2.59% and delayed peaks up to 30 min, while inundation depths reduced by 0.5 m in most areas, with inundation extent reduction at critical points in an urban area. The study demonstrated the utility of the coupled model for evaluating NFM strategies while emphasising the need for further validation and exploration of systematic interventions at larger catchment scales. By providing insights into the interactions between NFM interventions and catchment characteristics, this research contributes to the optimisation of flood risk management strategies and informs future policy development.

The number of individuals exposed to flooding is increasing and is projected to increase in the future. Catastrophic events like the July 2021 flood in Germany's Ahr Valley (Rhineland-Palatinate) illustrate the severe and often long-lasting mental health impacts such disasters can cause. However, research on the psychological consequences of extreme flooding remains less developed than studies on physical damage. Gaining a clearer understanding of individual mental burden following such events is essential for tailoring recovery efforts to address mental health needs effectively. This study investigates how various factors—including flood characteristics, circumstances of the recovery process, personal characteristics, perceptions, and sociodemographic characteristics—affect self-reported mental burden. Using binary logistic regression, we analyzed responses from 277 individuals affected by the July 2021 flood in the Ahrweiler district. Results show that even 18 months after the event, 42.6% of respondents continued to experience high to very high levels of mental burden. Interestingly, the analysis found that sociodemographic variables—particularly, health status—and personal characteristics and perceptions (e.g., persistent mental preoccupation) had a greater impact on mental burden than the characteristics of the flood or the reconstruction process. Considering the strong impact of health status, health monitoring of affected populations may help identify individuals at greater risk, ensuring timely and targeted mental health interventions. These findings underscore the importance of incorporating long-term psychosocial support into disaster recovery strategies.

Flood risk management (FRM) strategies in many developed countries increasingly focus on building flood resilience at property, community, and national levels. However, existing research on community flood resilience (CFR) has thus far inadequately addressed the social dynamics underpinning interactions among key resilience dimensions. Despite limited recognition of the social dimension, factors such as social capital and sociocultural dynamics remain insufficiently explored, warranting further investigation. This study employs a modified preferred reporting items for systematic reviews and meta-analyses (PRISMA) to critically review and synthesize research gaps, before presenting an innovative social capital oriented framework to evaluate CFR. While infrastructure, economic, environmental, human, and governance dimensions play significant roles, the proposed framework emphasizes the foundational role of social capital and sociocultural factors, including norms, values, and identities, in shaping resilience outcomes and actions. These factors influence the success or failure of resilience-building efforts, particularly in diverse, deprived communities, such as those with nonnative speaking populations. This innovative framework offers insights for multisectoral stakeholders, including flood risk managers, engineers, surveyors, property owners, and local authorities, to address persistent challenges in resilience-building activities and improve intervention outcomes.

Coastal cities face increasing flood hazards due to climate change. Physical infrastructures, such as levees, are commonly used to reduce flood hazards. To effectively manage flood risks, it is important to understand the degree to which physical infrastructures change both hazard and exposure. For example, many studies suggest that levee construction causes an overall increase in risk because levees promote exposure growth to a greater degree than they reduce flood hazards. Although this so-called “levee effect” is widely studied, there are knowledge gaps surrounding how uncertainties related to levee construction and flood risk translate into the occurrence and strength of the levee effect in coastal communities. Here, we use agent-based modeling to simulate the influence of flood risk information pathways on the dynamics around the levee effect, first under idealized conditions and then within a real-world coastal case study. We finally conduct a global sensitivity analysis to identify which model factors contribute to the strength of the levee effect. We find that, under idealized conditions, the strength of the levee effect is highly sensitive to economic (e.g., population growth) and engineering (e.g., levee failure) factors. However, under more complex coastal conditions, factors related to household behavior (e.g., risk aversion) are more influential on the strength of the levee effect. Overall, our findings emphasize the importance of capturing the interactions and uncertainties among multiple behavioral, economic, and engineering factors when measuring flood risk in coastal communities.

Floods are one of nature's most disturbing catastrophes, resulting in infrastructure damage, property devastation, and mortality. In Addis Ababa, flooding has significantly impacted residents and caused millions' worth of property damage in the last decade alone. It is continuously threatening and affecting city residents. This study focused on the spatial modeling of floods and the identification of areas susceptible to flood hazards in the city. Geographic information system (GIS) techniques combined with the information gain ratio (IGR) method were employed in this study. Five major flood hazard factors were identified: elevation, slope, rainfall, drainage density, and distance from drainage channels. The results show that 1.3% (7.1 km²) of the area is highly susceptible to floods, 29.4% (159 km²) is highly susceptible to heavy rains, 56% (302 km²) of the area is moderately susceptible, 12.5% (67.3 km²) of the area has low susceptibility, and less than 1% (4.2 km²) has very low susceptibility. Slope is the most influential factor (42.74%), followed by drainage density (28.21%), distance from drainage channels (18.8%), rainfall (7.69%), and elevation (2.56%). The sub-cities of Nifas Silk Lafto and Akaki Kality are the most susceptible to flood hazards; areas with steep slopes trigger high runoff during heavy rainy periods and cause flood hazards on gentle slope surfaces. It is recommended that to improve the accuracy of identifying susceptible flood-hazard locations, flooding simulation should be performed in conjunction with other variables and rainfall data (such as rainfall duration and intensity). Nevertheless, this research provides recommendations to municipal administration decision-makers regarding strategic management in the prioritization of flood-hazard zones.