Journal of Flood Risk Management最新文献

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.

There is an urgent need to assess the uncertainties in stormwater pipe design owing to the increasing occurrence of urban floods triggered by urbanisation and climate change. The design storm concept involves determining the event duration and corresponding depth. Various design hyetograph methods are available to partition the design storm depth into segments, which raises questions about their impact on stormwater system design. This study analysed an ensemble of eight widely used hyetograph methods, including triangular, linear/exponential, Chicago and alternating block, in industrial and residential urban catchments using the stormwater management model. The modelling results revealed clear disparities between hyetograph methods in terms of catchment hydrological response. Depending on the method used, the simulated outlet peak flow varied by ±30% in both catchments. As a result, outlet pipe sizes varied by one and two increments in the residential and industrial catchments, respectively. Almost no flooding was evident in the manholes using simple single-point hyetographs, whereas a quarter of the manholes showed flooding with more complex multipoint methods. Results underline the presence of high uncertainty in design flow estimates. Multipoint hyetograph methods should be used for designing critical infrastructure to minimise flooding risk if no local or regional data are available.

Three clustering algorithms, K-means clustering analysis (KCA), fuzzy cluster analysis (FCA), and density-based spatial clustering of applications with noise (DBSCAN), are applied to classify the 13 subbasins of the Mahe River, southwest India, based on 13 morphometric parameters of each. Suitable validation indices, such as Davies–Bouldin and Calinski–Harabasz indices, have been used to select the optimal number of clusters using KCA and FCA techniques. All three analyses have yielded three clusters, with subbasins 3–8 forming the first one. These constitute 23% of the total basin area of the Mahe. SW 12 forms a grouping of its own. The rest, SW 1–2, 9–11, and 13, form the third cluster. The first cluster corresponds to the subbasins identified as most susceptible to flooding. Cluster 3 encompasses the subbasins falling in the “Moderate” and “Least” categories with respect to the risk of flooding. The subbasin 12 (< 1 km²) exhibits a deviant morphometric pattern likely due to its specific topographical and network characteristics. The study reveals that cluster algorithms are effective in ranking and prioritizing subbasins of a river based on their potential for natural hazards like flooding. Moreover, the DBSCAN averts the use of cluster validation indices to determine the optimum clusters without compromising the results. All these methods would be beneficial in chalking out suitable management measures for different subbasins of a river based on their potential for any given hazard.

California's Central Valley is increasingly vulnerable to winter floods. A comprehensive spatial baseline of flood extents is critical for inundation analyses that can enhance future flood predictions, but cloud cover has prevented the regular observation of surface water extents with optical satellite imagery. In this study, we leveraged the daily resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to create a continuous series of monthly Dynamic Surface Water Extent (DSWEmod) images across the Central Valley from January 2003 to January 2023. We used the timeseries to assess the climatic driving forces of winter (Oct–April) surface water variability at sub-basin and pixel scales. At the sub-basin scale, we evaluated the influences of winter precipitation, occurrence of atmospheric rivers, and antecedent soil moisture on monthly surface water extents and found that the greatest correspondence occurs in mid-winter (Dec–Feb); in contrast, non-precipitation drivers such as water management play a stronger role in autumn and spring. The pixel-level analysis identified the probabilities of precipitation-driven surface water occurrences in the Sacramento basin are highest along rivers, conveyance channels, and floodways, with higher probabilities under wetter antecedent soil moisture conditions. Precipitation-driven surface water occurrences are also common in leveed areas and outside flood boundaries designated by state and federal agencies where exposure of structures to inundation was larger in terms of their value. Finally, areas with more frequent precipitation-driven flooding have poor recharge potential but are commonly within 5 km of areas classified as having good potential. This study demonstrates a novel approach for exploring the utility of MODIS for understanding surface water dynamics in mid-winter, a period characterized by peak precipitation, flood risk, and surface water extent. This information can provide valuable insights for (1) assessing flood risks for infrastructure and populations, (2) identifying areas most suited to strategic water management investments to increase recharge, and (3) analyzing precipitation thresholds that trigger flooding to allow proactive water management strategies to minimize damage and maximize recharge.

The July 2021 flood event severely affected western Germany, particularly Rhineland-Palatinate and North Rhine-Westphalia, with rainfall intensity and water levels exceeding the 100-year return period in many areas. This study analyzes the performance of flood monitoring and forecasting systems during this extreme event, focusing on the integration of meteorological and hydrological data within detection, monitoring, and forecasting processes. The findings reveal critical challenges in translating meteorological information into actionable flood warnings, including discrepancies between predicted and observed precipitation, underestimation of maximum discharges (exceeding the 100-year return period by up to 730% in some areas), and the absence of comprehensive flood forecasting systems in key regions. Approximately 60% of the investigated gauging stations experienced exceedances of all defined flood warning thresholds within 6 h or less, highlighting the rapid escalation of the event and the constrained response time for emergency measures. The results of the study underline the need to redesign flood information and warning systems: especially for extreme situations warnings must not be based only on measured data and not on single forecast values but mainly on potential risks and communicated uncertainty/probability.

In the United States, research to date has primarily focused on mitigating urban coastal flood risks, with limited knowledge available on the planning capacity and effectiveness of flood mitigation in rural inland areas. This study addresses the gap by analyzing 162 publicly accessible local comprehensive plans from local jurisdictions across Nebraska, evaluating their preparedness for managing floodplains and reducing flood risks. The research examines how well Nebraska's communities, particularly rural ones, are prepared for flood risk reduction and whether they have adopted more proactive measures following the historic 2019 flood disaster. The evaluation criteria focus on three key areas: vulnerability assessment, policy toolkits, and coordination mechanisms. The findings reveal a mix of strengths and weaknesses across these categories. Overall, vulnerability assessment is generally low, with a score of 26.7%, indicating limited awareness and inadequate use of federal and state floodplain datasets to support rural planning. Policy toolkits, scoring 58.7%, are moderately available, suggesting that local communities have taken broader steps to address local flood risk reduction. Coordination mechanisms are relatively well-established within local planning frameworks as found in 74.5% of the plans, showing promise for collaborative flood risk reduction efforts. In rural communities, particularly, the quality of plans is lower compared to urban communities. However, following the 2019 floods, rural communities have shown some improvement in enhancing floodplain management and planning. This research contributes to resilience planning theories, particularly for resource-limited and disadvantaged rural communities in the United States.