Journal of Flood Risk Management最新文献

Flooding in low-gradient river systems, particularly, within inland-to-coastal transitional settings, poses significant risks to both human and natural systems due to complex flow dynamics and the convergence of riverine, tidal, and storm-driven flooding regimes. Effective flood management strategies in these settings require a careful consideration of the interactions between tributary flows and the main river, as the synchronization of peak flows can intensify flood severity. Using a counterfactual hydrodynamic modeling approach, this study investigates how modifications to magnitudes and timings of tributary inflows, as part of practical flood mitigation interventions, influence flood dynamics and overall flood risk along the Vermilion River in south Louisiana, USA—a representative a case study. Simulations of various tributary desynchronization scenarios showed that, while managing individual tributary flows can lower water levels in the main river, simultaneous manipulation of multiple tributaries can introduce added complexities, since poorly timed tributary interactions could diminish the intended flood mitigation benefits. The analysis also revealed that alterations to tributary hydrographs can modify existing flow exchanges between the river and interconnected large natural storage systems, such as swamps, highlighting the importance of comprehensive flood management strategies that consider different flood dynamics in low-gradient river systems. Overall, this study offers actionable insights for optimizing flood management strategies in similar systems worldwide, where intricate interdependencies among tributaries, natural storage features, and main rivers can influence flood mitigation outcomes.

This paper proposes a novel methodology for peak flow estimation. This methodology uses single-event hydrological modeling based on the software HEC-HMS and curve numbers (CNs) estimated as a function of antecedent and current weather variables and is applied to the river Brembo case study in Northern Italy. By using rainfall, weather, and water discharge data collected over an eleven-year-long period, from 2013 to 2023, HEC-HMS is first used to optimize the CN values at two cross sections in the Brembo basin, in an attempt to reproduce the flood peak in numerous single rain events. Then, regression equations are constructed to express CN as a function of current event rainfall depth and antecedent rainfall depth and temperature, as explicative variables for current soil conditions. The good predictive performance of HEC-HMS based on CN values estimated through the regression equations (for the peak flow at the two cross sections, a mean absolute percentage error [MAPE] of 0.26 and 0.29, respectively, in calibration, and 0.33 and 0.45, respectively, in validation; and an index of agreement [d] of 0.84 and 0.92, respectively, in calibration, and 0.86 and 0.88, respectively, in validation) makes the modeling tool constructed in the paper efficient and effective for potential early-warning applications.

This study presents a new flood routing method integrating the modified Muskingum (NLM7_Aqlat) method with hybrid natural optimization algorithms (hybrid of Humboldt squid optimization algorithm [HSOA] and gradient-based optimizer [GBO] and hybrid of Pine cone optimization algorithm [PCOA] and GBO). In the NLM7_Aqlat, the lateral flow is applied to a seven-parameter nonlinear Muskingum model (NLM7), and hybrid natural-based optimization algorithms optimize the parameters. In Karahan flood routing, the standard value of the mean sum of squared deviations (SSQ_mean) for integrating the NLM7_Aqlat model and PCOA_GBO was calculated to be 96.06% less than the other 10 algorithms (such as GA and GBO). In Wilson flood routing, the PCOA_GBO algorithm in the NLM7 model calculated the SSQ_mean criterion value 99% lower than other optimization algorithms. The HSOA_GBO algorithm in the NLM7_Aqlat model provided the best flood routing for Weisman-Lewis, enhancing hydrograph accuracy. In Karun flood routing, the PCOA algorithm estimated the SSQ_mean in the NLM7 model to be 89% lower than other algorithms. The new flood routing method showed competitive results versus NLM7. Hybrid optimization algorithms outperformed standalone ones, prompting authors to recommend this methodology for enhancing early flood warning systems.

A laboratory-based physical modeling environment has great potential to reproduce the complex physical hydrologic phenomena and understand the interactions of rainfall-runoff processes in a visual and informative manner. In this study, a three-layer physical modeling environment was developed to represent the dynamics of runoff production from the urban drainage system. The three-layer physical modeling environment consists of a rainfall simulator (the 1st layer), a surface drainage network (the 2nd layer) and a subsurface rainwater pipe network (the 3rd layer). The degree of homogeneity of the spatial rainfall distribution produced by the rainfall simulator ranged from 78.6% to 84.0%, which lies within an acceptable range in the rainfall uniformity. The physical catchment model accurately represented the dynamic characteristics of the catchment response in a natural system associated with differing rainfall intensities within a controlled laboratory modeling environment, particularly the magnitude, volume, and shape of the discharge hydrographs. The three-layer physical modeling setup was implemented to identify the effects of stormwater management facilities such as the rooftop detention storage and the permeable road pavement on the urban rainfall-runoff responses. The runoff reduction rates for the peak discharge and the total discharge volume showed a strong linearity with the percentage coverages of the stormwater management facilities. Functional relationships between the variables were established to provide intuitive criteria for the runoff reduction rates for a specific coverage percentage of the rooftop detention storage and the permeable road pavement. These results demonstrate the effectiveness of the three-layer physical setup for modeling rainfall-runoff processes within the urban drainage network.

Amidst intensifying climate change, flash floods are becoming more recurrent, posing significant threats to safety and assets, especially in mountainous areas. Given the non-negligible influence of bridges on flash floods, this research capitalized on fluid dynamics simulations to examine the mechanisms by which six bridges within the investigation zone affect the evolution of flash floods. Moreover, bridge blockage from debris accumulation was methodically investigated under multiple return periods. Results indicated that during the two historical floods, the bridges altered the distribution pattern of flash floods from various flood elements, including the backwater effect, flow velocity, and inundation. It is noteworthy that the spillway bridge (M1) notably raised water levels and slowed flows, whereas the influence of other bridges on flood dynamics was more muted. The presence of six bridges resulted in expanded flooded areas, particularly near the upstream bridges, raising risks for Qishi Village. Furthermore, the increasing blockage ratios at bridge B2 during multiple return periods exacerbated the impacts on flood elements, consequently amplifying the disaster of flash floods. This research strongly emphasizes the importance of incorporating bridges and their blockages into flood risk management. It further provides technical insights to bolster the basin's resilience against extreme hydrological events.

This review, based on 231 articles, focuses on studies relevant to Canada that assess fluvial, pluvial, and coastal flood hazards at national and broader scales. It evaluates the application of remote sensing and artificial intelligence methods for flood mapping within the Canadian context. The review highlights a growing trend in large-scale flood modeling, with increasing relevance for Canadian flood risk management. Methods for downscaling coarse-resolution flood estimates from physically based models to finer spatial scales are particularly important for Canada's diverse hydrological regions. Global estimates of flood defense standards often rely on socio-economic indicators, but for Canada, physical hazard factors should also be integrated. Advances in LiDAR and radar remote sensing have improved the accuracy of Canadian flood models by providing detailed topographic data. Artificial intelligence techniques show strong potential for predicting flood inundation and enhancing flood hazard mapping across Canadian landscapes.

Bangladesh is a flood-prone country, yet studies on multidimensional vulnerability assessments related to floods remain limited. This study evaluates social, economic, physical, institutional, attitudinal, and gender vulnerabilities in Paschim Machimpur and Purba Machimpur, two rural flood-prone areas in Sunamganj District. Primary data were collected from 487 households through structured questionnaires and face-to-face interviews. The data were analyzed using descriptive statistics, chi-square tests, the Mann–Whitney U-test, and a composite vulnerability index. The analysis reveals disparities in household characteristics, including size, age, education, and the presence of vulnerable members such as children and older people. Economic assessments show high dependency on agriculture and fishing, with many households lacking defined income sources and flood insurance. Physical vulnerabilities include poor housing materials and inadequate sanitation. Institutional vulnerabilities highlight deficiencies in early warning systems and flood preparedness. Attitudinal assessments reveal a lack of confidence in authorities' flood risk reduction programs, and gender vulnerabilities show women are disproportionately affected due to social and cultural factors. Despite similar overall vulnerability scores between the two areas, Paschim Machimpur shows higher composite vulnerability. The study calls for enhanced community engagement, better communication of flood risks, and the development of more robust early warning systems to reduce flood vulnerabilities.

This study evaluates the flood risk of households in the flood-prone Char areas of Dewanganj, Jamalpur, Bangladesh, focusing on sociodemographic factors, vulnerability, flood exposure, and capacity. Using survey data from 400 households, we found a predominance of male-headed households, a high reliance on wage labor, and widespread substandard housing, all contributing to flood risk. The findings reveal a rise in flood frequency and home inundation; nearly all respondents (99.75%) confirm worsening flood conditions, indicating a long-term climate trend rather than isolated events. Vulnerabilities are further heightened by low education levels (60.75% with no formal education), high poverty rates (98.75% below the national income average), and limited access to critical resources like durable housing and flood preparedness training. Regression analyses indicate significant associations between flood risk and factors like age, income source, and housing type (female-headed households: p < 0.001, β = 0.06; age groups 36–45 and 46–55: p < 0.001, β = 0.07, and β = 0.11, respectively). The study highlights the need for targeted interventions, with the most critical recommendation being the improvement of housing resilience, especially for vulnerable groups such as female-headed households and those in temporary structures. Enhanced flood forecasting, resilient infrastructure, and community-based training programs are also essential for reducing flood risk and increasing adaptive capacity. Our findings provide actionable insights for policymakers and NGOs to develop tailored flood resilience strategies, offering a foundational model for flood-prone communities across Bangladesh.

In Bangladesh, where floods frequently occur, there is a severe annual risk to community displacement, agriculture, fisheries, livestock, public health, and food security. Extreme flooding events are becoming more common due to a combination of human-induced climate change, irregular upstream river water flows, increased proportion of sediment distribution on the riverbed, institutional fragility, lack of planning regulations, and changing rainfall patterns. Effective flood management requires precise and timely flood mapping methodologies to adopt disaster risk reduction strategies and enable efficient response efforts. This study presents an approach that facilitates timely flood identification, improving emergency response, evacuation initiatives, relief distribution, and disaster risk reduction. This research introduces a novel methodology for expedited flood inundation mapping, using the August 2022, 2023, and especially the 2024 flood events in the Feni District as the primary case study. The study employs Google Earth Engine (GEE) and Sentinel-1 synthetic aperture radar (SAR) data to accurately delineate flood inundation regions by utilizing vertical transmit and vertical receive (VV), vertical transmit and horizontal receive (VH), and VV/VH polarization bands. Water bodies characterized by lower backscatter values in VH polarization ranging from −41.15 to −24.06 dB and VV polarization from −31.66 to −15.94 dB were identified as suitable thresholds for flood inundation area delineation. To assess the accuracy of flood map, this study focuses on pixel-based digital classification and machine learning (ML) techniques separately for flood inundation mapping. The classification accuracy values of 95.60% for the pixel-based method and 94.40% for the random forest ML model specifically correspond to the 2024 flood event. This study developed a GEE-based operational methodology by evaluating two innovative techniques designed for rapid flood inundation mapping to support effective flood management and disaster risk reduction efforts.

Flood management in urban areas requires innovative and adaptive strategies to address the growing challenges posed by climate change, land use transformations, and socio-economic developments. This study provides a comprehensive analysis of the complex interactions within an urban area using integrated modeling approaches, offering critical insights into potential future challenges. These approaches incorporate climate, land use, hydrological, hydraulic, and damage models, complemented by an adaptation pathways map and recommendations for the most effective strategies, conducted in a flood-prone urban area. The analysis projects an increase by 2080 of 3.39°C in temperature, 46% in precipitation, and 29% in flood-related damages. The study underscores the substantial impact of land use changes on flood damages, highlighting the need for integrating land use planning into flood mitigation strategies. A rigorous evaluation identified a combination of measures—including concrete dyke construction, dredging, afforestation, and forest conservation—as effective actions for mitigating flood risks in the region near the Caspian Sea. Forest conservation and afforestation reduce peak flood discharge by 12.2% and 23.1%, respectively, for a 100-year return period. Economic evaluations were performed for all adaptation pathways to assess their feasibility and cost-effectiveness. Using the Analytic Hierarchy Process (AHP), the study determined that the optimal strategy is the simultaneous implementation of concrete dyke construction, dredging, and forest conservation. Additionally, six adaptation pathways were defined through the Dynamic Adaptive Policy Pathways (DAPP) method to provide a structured roadmap for implementing and adjusting flood management measures over time. These pathways aim to reduce potential future flood damage to negligible levels. Overall, this research highlights the importance of adopting integrated and adaptive strategies to address the multi-faceted challenges posed by environmental changes, ensuring effective flood management amidst growing deep uncertainties.