Journal of Flood Risk Management最新文献

Asher, M., M. Trigg, S. Böing, and C. Birch. 2025. “The Sensitivity of Urban Pluvial Flooding to the Temporal Distribution of Rainfall Within Design Storms.” Journal of Flood Risk Management 18, no. 3: e70097. https://doi.org/10.1111/jfr3.70097.

In the list of authors for the paper, Steven Böing was incorrectly listed as Steven Boïng.

The online version of this article has been corrected accordingly.

We apologize for this error.

The integration of building-level floodproofing into flood risk management frameworks is gaining increasing recognition. As property owners ultimately decide on implementation, and financial incentives can drive adoption, a critical gap remains: the absence of Building-Specific, Context-Sensitive, Micro-Scale Risk Assessment (BC_MRA) frameworks that effectively support property owners and policymakers in their decision-making. This study introduces a BC_MRA framework alongside a straightforward yet expandable risk-based incentive structure, representing an innovative approach to enhancing property-level floodproofing, hereby advancing flood resilience research. A key contribution is a systematic methodology that contextualizes all the components of micro-scale flood risk assessment and the process for assessing the effectiveness of floodproofing interventions. The framework is applied to a case study in Pesaro, Italy, where dry and wet floodproofing strategies' financial viability and risk reduction potential are evaluated in response to riverine flood risk. Results underscore the importance of BC_MRA to inform effective micro-scale flood mitigation, revealing that expected annual damage is not solely dependent on proximity to the river but is also significantly influenced by building-specific vulnerability to flooding. Furthermore, wet floodproofing consistently resulted in longer payback periods compared with dry floodproofing, rendering it economically unviable for any of the buildings studied.

Forest cover within catchments is a widely adopted Nature-based Solution (NbS) for flood mitigation, offering hydrological benefits such as rainfall interception, enhanced infiltration, and reduced overland flow. Despite its recognized potential, quantitative reviews remain limited, especially at the catchment scale, with effectiveness varying by spatial scale, forest type, and climate. This review synthesizes 50 international case studies involving forest-based NbS, selected through structured screening based on intervention type, catchment characteristics, and availability of quantitative flood metrics, and presents a detailed bibliometric and content analysis. Forest cover consistently impacts peak flow across catchments of all sizes, with a generalized linear relationship where the effect magnitude is approximately half the forest cover change. For example, a 20% increase in forest cover tends to reduce peak flow by 10% across small, medium, and large catchments. Across a range of catchment sizes, there are only minor differences in the mean peak flow reductions for different event intensities (up to 1% AEP). An asymmetric hydrological response is evident: deforestation consistently increases peak flows, whereas afforestation yields gradual reductions, which are shaped by forest maturity, spatial distribution, and modeling assumptions. Upstream distributed forest placements offer distinct hydrological benefits. These outcomes highlight the importance of conserving mature forests, preventing deforestation, and optimizing forest placement, while acknowledging potential adverse impacts on water availability during dry periods.

The World Bank is a leading global institution for disaster risk management, the bulk of which is dedicated to flood risk management (FRM). Due to the Bank's power as a lending agency and the global distribution of flood risks it has addressed, the Bank's project financial agreements (FAs) are an expression of a power relationship worthy of detailed investigation. These FAs present an opportunity in which the Bank could impose its policy preferences and set the parameters for FRM in recipient countries, thus illuminating both an important driver for change and the Bank's fundamental modus vivendi. This paper uses qualitative content analysis to investigate 52 FAs from 1975 to 2023, searching for patterns in the FRM measures they emphasise. We examine how FRM measures advocated by the Bank have changed over time, finding that the Bank has used its power to promote early adoption of integrated structural and non-structural FRM strategies in a mutually reinforcing complementary arrangement. The Bank advanced integrated FRM approaches well before other international bodies and national agencies and thus features as a world leader in this respect. We also find that common criticisms of neoliberalism and gender equality against the Bank are not entirely unfounded, but progress has occurred in these directions in recent years.

Zhang, G., Q. Chen, Y. Wang, Z. Li, Y. Zhou, and Z. Jin. 2025. “Trend Analysis of Discharge and Water Level Changes in the Fluctuating Backwater Area.” Journal of Flood Risk Management 18, no. 3: e70096. https://doi.org/10.1111/jfr3.70096.

The authors spelled a wrong number of the foundation. The number should be corrected from 2023YFC3209509 to 2023YFC3209505.

Funding

This work was supported by National Key Research and Development Program of China (2023YFC3209505, 52479058 and 52409082); Basic Research Operation Funds Project of the Central-level Research Institutes in China (CKSF2024324); Scientific Research Project of China Three Gorges Corporation (0704230).

Acknowledgments

This study was supported by the National Key Research and Development Program of China (2023YFC3209505, 52479058 and 52409082); Basic Research Operation Funds Project of the Central-level Research Institutes in China (CKSF2024324); the Scientific Research Project of China Three Gorges Corporation (Grant No. 0704230).

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Flood shelters are crucial for mitigating flood impacts, providing temporary refuge. However, their effectiveness hinges on strategic placement near flood-prone areas, guided by accurate risk maps. Traditional flood risk analysis fails to distinguish floods based on their extent and duration, even though they have varying impacts. This study introduces a novel approach to flood risk mapping by creating maps specific to varying flood severity levels, offering a more precise understanding of spatial risk distribution compared to conventional methods. By classifying floods and computing hazard for each severity category, it provides a detailed understanding of relative hazard dynamics and their spatial variations. We further compute risk by combining hazard, vulnerability, and exposure at block level for each flood category. These category-specific risk maps highlight how risk differs across flood types at a granular level, demonstrating the benefits of such classification for tailored risk assessments. Analysis of categorized risk maps alongside current shelter locations reveals disparities between hotspots and shelter placements, highlighting the importance of effective shelter location and evacuation planning based on localized risk assessment. Fine-scale risk information is vital for informed community-level flood mitigation. The developed method offers a generalizable approach for categorizing risk maps across various spatial scales and global locations.

Flood risk managers seek to optimise Blue-Green Infrastructure (BGI) designs to maximise return on investment. Current systems often use optimisation algorithms and detailed flood models to maximise benefit–cost ratios for single rainstorm return periods. However, the BGI scheme optimised for one return period (e.g., 100 years) may differ significantly from those optimised for others (e.g., 10 or 20 years). This study aims to assess the effectiveness of single return period-based BGI design across multiple storm magnitudes and introduces a novel multi-objective optimisation framework that simultaneously incorporates five return periods (T = 10, 20, 30, 50 and 100 years). The framework combines a non-dominated sorting genetic algorithm II (NSGA-II) with a fully distributed hydrodynamic model to optimise the spatial placement and combined size of BGI features. For the first time, direct damage cost (DDC) and expected annual damage (EAD), calculated for various building types, are used as risk objective functions, transforming a many-objective problem into a multi-objective one. Performance metrics such as Median and Maximum Risk Difference (MedRD, MaxRD) between reference and trial Pareto fronts, capturing characteristic single values from the distribution of risk differences, and the Area Under Pareto Front (AUPF), indicating overall optimisation quality, reveal that a 100-year optimised BGI design performs poorly when evaluated for other return periods, particularly shorter ones. In contrast, a BGI design optimised using composite return periods enhances performance metrics across all return periods, with the greatest improvements observed in MedRD (22%) and AUPF (73%) for the 20-year return period, and MaxRD (23%) for the 50-year return period. Furthermore, climate uplift stress testing confirms the robustness of the proposed design to future rainfall extremes. This study advocates a paradigm shift in flood risk management, moving from single maximum to multiple rainstorms-based optimised designs to enhance resilience and adaptability to future climate extremes.

One of the characteristics of flooding in coastal areas is that it can be induced by different climatic drivers such as storm surges, wave run-up, rainfall, and/or river flow, each of which can act individually but are also often interconnected. In addition, when flooding is induced by marine drivers impacting sedimentary coastlines, erosion also occurs, which can significantly increase flooding. This is likely to intensify in a climate change scenario in which sea-level rise will directly and indirectly increase flooding in coastal areas. In addition, the concentration of population, infrastructure, and urbanization significantly increases the exposure of these zones. All this results in a very high-risk area, which has been dramatically illustrated during the last decades by the impact of extreme events that have caused great damage in coastal areas around the world either in singular events (e.g., Xynthia in 2010, Sandy in 2012, Gloria in 2020) or by accumulation during a season (winter 2013/2014 in the Atlantic coast of Europe).

Tropical islands are also very high-risk areas. Climate change is impacting these islands severely, with powerful hurricanes observed in the West French Indies over the last decades, for example. And in the long term, sea level rise is/will impact such islands, sometimes erasing them from the world map. At the same time, these high-level risk areas are most often poorly equipped with sensors to predict risks, to alert the population, and to manage adequately the crisis and retrofit phases. Specific tools, toolboxes, and resilience strategies have to be designed for such specific territories, which may be isolated and where several islands at different development levels are part of the same archipelagos. Such specific geographies can be seen as aggravating factors, or on the contrary, as a model to test different resilience strategies because these areas are small and can be modeled and monitored maybe in an easier manner.

With such a broad subject matter, this special issue covers a range of topics from understanding the processes involved to developments in risk analysis methodology, event monitoring, case studies, and advances in knowledge related to these topics.

This special issue is composed of six articles covering six coastal regions of the world and addressing key points aligned with the themes of flood risk and resilience in coastal zones and tropical islands.

The article entitled ‘Effect of river cleaning on lowland drainage in South-Eastern Sumatra’ (Aprialdi et al., 2023) presents concrete results that address the challenges of coastal and island risks:

In tropical coastal regions, flood risks are exacerbated by the combined effects of climate change (sea level rise, increased rainfall) and local dynamics such as land subsidence. This article examines the case of south-eastern Sumatra (Indonesia), a coastal marshland area heavily affected by tides and logging (eucalyptus p

Under the operation of the large reservoir, the variation law of water level in the fluctuating backwater area is complex, which causes river protection engineering to lack a theoretical basis. The changing trend of daily water level in the fluctuating backwater area of the Three Gorges Reservoir (Cuntan hydrological station) was calculated, based on the relationship between daily discharge and water level, and the flow duration curve method. From 2002 to 2021, the daily water level processes had a distinct plateau stage after the flood season since 2008. The water level processes were composed of two parts, including the natural period (2002–2008) and the response period (2009–2021). The average daily discharge increased from 10214.93 m³/s to 10893.38 m³/s, and the average water level increased from 163.87 m to 169.03 m since 2008. The coefficient parameter of the relationship between daily discharge and water level decreased from 0.041 to 0.026, which indicates that the effect of daily discharge variation on the water level change was weakened. The maximum flood discharge and water depth increased by 29.82% and 27.21%, respectively, which led to a higher flood risk in the fluctuating backwater area. In this study, we proposed a novel approach to test trend change in the relationship between daily discharge and water level, which can be generalized to rivers influenced by human activities. Combining the trend test method and flow duration curve method, the characteristic daily discharge and water level can be calculated to guide engineering projects.

Floods can cause significant damage to land, infrastructure, and individual well-being. In the Canadian prairies, flood is a recurring natural disaster for farmers and ranchers. The flat terrain and extensive agricultural lands make the region vulnerable to flooding. Climate change could alter hydrological processes, leading to an increase in both frequency and intensity of flood events. In this study, machine learning and hydrodynamic models were combined to predict flood risks on agricultural lands based on various possible climate change scenarios. For this research, outputs from CanESM2, SDSM, ANN, HEC-GEORAS, and HEC-RAS were integrated to generate 2D flood simulation outputs. Climate change models CanESM2 and SDSM were used to simulate the possible future temperature and precipitation regimes (RCP 8.5 and RCP 4.5). The Artificial Neutral Network (ANN) model was used to predict possible future snowfall levels based on simulated precipitation and ambient air temperature regimes. The second ANN was further trained with first ANN data to predict possible flow rates in the river. A flood-frequency analysis was conducted using 10, 50, and 100 years flood return periods. The collective data output was used in HEC-RAS to simulate flooding under respective return periods. The georeferenced vector and raster data were generated using ArcGIS and HEC-GEORAS. Comparative flood simulation outputs were generated using historical data. The flood simulation results using historical data were compared to climate change conditions. The results indicate that climate change could potentially exacerbate the severity of floods in agricultural lands across the prairies. The greater return periods correspond to greater flood depths, velocities, and inundation areas, with RCP 8.5 creating the most extreme conditions. In addition, climate change could potentially accelerate peak flows in the river and increase hydrological pressure.