Flood Emergency Research Articles

Assessing resilience through social networks: A case study of flood disaster management in China

The study of resilience has gained significant traction in the field of disaster risk reduction (DRR) due to the escalating frequency and intensity of extreme weather events such as floods. Social capital accumulated within social networks comprising stakeholders serves as a crucial resource for the establishment of disaster resilience. Therefore, this study examines and evaluates resilience to changes within DRR process through a social capital perspective. First, it embraces the conceptual framework of disaster resilience through social capital and elucidates six distinct forms of social capital relevant to enhancing disaster resilience. Subsequently, the governance structure of flood emergency management in China is thoroughly examined and a disaster resilience assessment system is established from the standpoint of social capital using a social network analysis methodology. Finally, using the case of the devastating flooding triggered by the 7–20 rainstorm in Henan, China, resilience levels are measured at various stages of the flooding employing 4R theory. The results of this study will aid governmental and non-governmental organizations in enhancing their resilience in the face of natural disasters.

Research on nowcasting prediction technology for flooding scenarios based on data-driven and real-time monitoring.

With the impact of global climate change and the urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction of flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapid nowcasting prediction method of urban flooding based on data-driven and real-time monitoring. The proposed method firstly adopts a small number of monitoring points to deduce the urban global real-time water level based on a machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with real-time monitoring data and high-accuracy precipitation prediction. The results show that the average MAE and RMSE of the urban flooding and conduit system in the deduction method for water level are 0.101 and 0.144, 0.124 and 0.162, respectively, while the flooding depth deduction is more stable compared to the conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R2 are as high as 0.973 and 0.962 of testing. The urban flooding nowcasting prediction method provides technical support for emergency flood risk management.

Media and Natural Disasters: Organising Storytelling in the Age of Climate Change

Starting on 2 November 2023, some territories in the Region of Tuscany in Italy were hit by exceptionally intense meteorological and calamitous events. The Region of Tuscany’s government was immediately at the forefront of relief, assisting the population, and aiding restoration. On 3 November, a national state of emergency was declared due to flooding. Journalistic communication is an essential aspect of disaster mitigation, preparedness, response and recovery. In terms of protecting people and reducing damage, journalists and the media have an important role to play. This article reports an Italian case study analysing the behaviour of local media in cases of natural disaster. Nine focus groups were conducted with local journalists covering the flood emergency. The results highlight the role of social and institutional mediation, rather than mere dissemination, played by the local press in emergency situations, a central element in the construction of a community bond, precisely in moments of insecurity and disorientation. The narration of a disaster from the inside seems to have allowed the emergence and representation of hitherto unknown social realities in Tuscany. The goal of the news coverage was widened, making it possible to respond better to the diverse interests of the reading public and to satisfy in a short time and exhaustively the information needs of individual communities in difficulty.

Threshold and real-time initiation mechanism of urban flood emergency response under combined disaster scenarios

Scientific and reasonable emergency response initiation mechanisms can provide important support for decision making regarding the emergency management of urban floods. However, there is a lack of a unified paradigm on how to calculate the threshold for emergency response initiation and reasonably initiate emergency response. Therefore, this study proposes a loss-driven urban flood emergency response initiation framework from the perspective of combined disasters. A discrimination mechanism of the emergency response initiation level was established based on the optimal threshold and loss function. And the rainfall event that occurred in Zhengzhou, China, on July 20, 2021, was taken as an example to realize real-time emergency response discrimination and initiation driven by forecast data. Results showed that the initiation time of the Level I emergency response using the proposed method was 9.5 h earlier than the time of the government release, thereby significantly increasing the preparation time for flood management personnel. In addition, the results of the optimal threshold selection indicated that the Natural Breakpoint method was the optimal method for loss threshold partitioning, with the comprehensive evaluation index (CEI) being 3.56–9.53 % higher than those of the K-means, Equal Interval, and Quantile method. These results constitute a reference for urban emergency management and related research.

Designing user-centered decision support systems for climate disasters: What information do communities and rescue responders need during floods?

Flooding events are the most common natural hazard globally, resulting in vast destruction and loss of life. An effective flood emergency response is necessary to lessen the negative impacts of flood disasters. However, disaster management and response efforts face a complex scenario. Simultaneously, regular citizens attempt to navigate the various sources of information being distributed and determine their best course of action. One thing is evident across all disaster scenarios: having accurate information and clear communication between citizens and rescue personnel is critical. This research aims to identify the diverse needs of two groups, rescue operators and citizens, during flood disaster events by investigating the sources and types of information they rely on and information that would improve their responses in the future. This information can improve the design and implementation of existing and future spatial decision support systems (SDSSs) during flooding events. This research identifies information characteristics crucial for rescue operators and everyday citizens' response and possible evacuation to flooding events by qualitatively coding survey responses from rescue responders and the public. The results show that including local input in SDSS development is crucial for improving higher-resolution flood risk quantification models. Doing so democratizes data collection and analysis, creates transparency and trust between people and governments, and leads to transformative solutions for the broader scientific community.

Deploying Mobile Applications for Emergency Flood Response in Geographically Isolated Areas: A Data-Driven Approach

Deploying Mobile Applications for Emergency Flood Response in Geographically Isolated Areas: A Data-Driven Approach

The Influence of Future Changes in Tidal Range, Storm Surge, and Mean Sea Level on the Emergence of Chronic Flooding

AbstractSea‐level rise is leading to increasingly frequent coastal floods globally. Recent research shows that changes in tidal properties and storm surge magnitudes can further exacerbate sea‐level rise‐related increases in flood frequencies. However, such non‐stationarity in tide and storm surge statistics are largely neglected in existing coastal flood projection methodologies. Here we develop a framework to explore the effect that different realizations of various sources of uncertainty have on projections of coastal flood frequencies, including changes in tidal range and storminess. Our projection methodology captures how observed flood rates depend on how storm surges coincide with tidal extremes. We show that higher flood rates and earlier emergence of chronic flooding are associated with larger sea‐level rise rates, lower flood thresholds, and increases in tidal range and skew surge magnitudes. Smaller sea‐level rise rates, higher flood thresholds and decreases in sea level variability lead to commensurately lower flood rates. Percentagewise, changes in tidal amplitudes generally have a much larger impact on flood frequencies than equivalent percentagewise changes in storm surge magnitudes. We explore several implications of these findings. Firstly, understanding future local changes in storm surges and tides is required to fully quantify future flood hazards. Secondly, existing hazard assessments may underestimate future flood rates as changes in tides are not considered. Finally, identifying the flood frequencies and severities relevant to local coastal managers is imperative to develop useable and policy‐relevant projections for decisionmakers.

Analisis Instrumen Model Mitigasi Banjir di Indonesia

One of measures to reduce the flood impact in Indonesia has been done by flood emergency response action plan. The action plan is generated from decision making process assisted by instrument such computer models which have capability in producing scenarios and decision to be taken. This research is conducted to identify how the written procedures (delivered by international and national agencies) related to flood emergency response, being accommodated by those (computer) models. By using qualitative analysis, the element procedures of an effective flood emergency response model have been reviewed. The result shows that the detailed information such human and material resources, facilities information, and warning time-lag, have not been properly and entirely accommodated by the (computer) model. Highlights are also given to propose more effective instrument to improve the management of flood crisis.

Assessment and influencing factors of urban residents' flood emergency preparedness capacity: An example from Jiaozuo City, China

Public flood emergency preparedness capacity (EPC) is a key factor to reduce casualties and property losses in flood disasters. In this study, a flood EPC index of urban residents was constructed using four indicators (emergency knowledge, skills, awareness, and supplies) and used to assess the flood EPC of urban residents in Jiaozuo City, Henan province, China. Ten key factors influencing residents’ flood EPC were identified based on literature review and with the help of experts, and the impacts of influencing factors on EPC were analyzed using variance analysis (ANOVA) and post hoc multiple comparisons. The results showed that the flood EPC of urban residents in Jiaozuo City was at a medium level, and the numbers of respondents with low, medium and high levels were 21.03 %, 62.62 % and 16.35 %, respectively. The impact analysis of influencing factors to urban residents' flood EPC showed that (1) most influencing factors had significant impacts on residents' flood EPC except for gender and occupation. (2) The respondents with higher education, higher incomes, more family members, mobility-impaired family members, and more flooding experiences had higher flood EPC than others. (3) The age group between 26 and 50 had higher flood EPC than other age groups. (4) The respondents living on bungalow and near a river had higher flood EPC than others. The findings of this study can provide useful information for the local government to develop urban flood management policies and for urban residents to improve their flood EPC.

Integrated Decision Support System for Flood Disaster Management with Sustainable Implementation

Emergency management of a flood catastrophe should involve not only an immediate reaction to the flooding but also the pre-flood disaster phase, the flood disaster phase, and the post-flood disaster phase. This is in addition to the immediate response to the flooding. This research investigates whether or not a decision support system (DSS) for flood emergency management is necessary and whether or not it is feasible. Previously, we presented a model for the development of modular disaster management decision support systems. The prior work that we did on the integration of DSS model decomposition-derived modular subroutines is expanded upon in this research. The act of merging many models into a single comprehensive logical model is referred to as “Observed and Forecasted.” After that, it offers a systematic framework for handling crises throughout the whole of the process, and it concludes with an in-depth analysis of the function architecture of each application subsystem. In this work, we create a disaster management metamodel with the goal of eventually building a disaster management (DM) language. It will function as a representational layer of DM data that, over time, will create a system for DM decision support that mixes and matches various DM actions in line with the evolution of the disaster as it develops. A decision support system that will contain an innovative metamodel as a core component will be developed in order to accomplish the aims of integrating, enabling, and accelerating access to DM information. These objectives will be reached via the creation of a decision support system.

面向应急防汛的车载水文监测与指挥调度系统

面向应急防汛的车载水文监测与指挥调度系统

Optimasi Rute Distribusi Bantuan Logistik Kesehatan ke Daerah Rawan Bencana Banjir di Kota Yogyakarta

In the context of flood risk mitigation in Yogyakarta City, particularly in the districts of Gondokusuman and Umbulharjo, this study highlights the significant impact of geographical factors and high rainfall intensity. Floods not only cause damage to infrastructure but also pose a serious threat to public health. Flood-prone areas often become breeding grounds for diseases that can spread rapidly, especially in emergency conditions. This research aims to identify optimal routes for the distribution of health logistics in dealing with flood disasters in Yogyakarta City. Using the Simulated Annealing (SA) algorithm, the study compares the optimization results of distribution routes without clustering and with clustering (based on districts). Findings indicate that the use of clustering is effective in minimizing the distance and travel time of distribution, with the clustering scenario emphasizing time efficiency. The contribution of this research lies in the development of responsive and efficient health distribution strategies during flood emergencies. Thus, the implementation of the SA algorithm and clustering can serve as a foundation for better emergency response planning, providing tangible benefits for those involved in flood disaster management in the region.

Assessing knowledge of emergency preparedness and its association with social demographic characteristics among people located in flood-prone areas of Chibavi and Chiputula in Mzuzu City, northern Malawi

Knowledge of flood preparedness ensures that community members in flood-prone areas properly prepare for floods and respond effectively when flooding occurs. Limited research has been conducted on knowledge of flood preparedness in flood-prone areas in Mzuzu City in the northern part of Malawi. Therefore, the study aimed to explore the knowledge of emergency preparedness and associated social demographic characteristics among people in Chibavi and Chiputula's flood-prone areas in Mzuzu City, Northern Malawi. A cross-sectional descriptive design utilising a multistage sampling technique was used to recruit 241 participants in 2018. A structured interviewer-administered questionnaire was used to collect data. The questionnaire was adapted from the Measuring Community Awareness and Preparedness Model and validated with Cronbach's Alpha of 0.847. SPSS version 16 was used for analysing percentages and frequencies to determine the proportion of people with knowledge of emergency preparedness. Chi-Square and Fisher's Exact Test were used to measure the association between social demographic characteristics and knowledge of emergency preparedness. Findings indicated that 61.4 % of participants lacked knowledge of emergency preparedness, and there was a significant association (0.044) between the level of education and knowledge of emergency preparedness. Although males were more knowledgeable than females, there was no significant association between gender and knowledge (0.712) and marital status and knowledge (0.581). This research showed that people in the two settings in Mzuzu City needed knowledge of emergency preparedness for floods. There is a need for clear plans to teach communities and families about emergency flood preparedness to ensure a common understanding of emergency preparedness, which will enhance collaboration among community members and prevent unnecessary deaths.

Flood Impact Assessment in Koton Karfe Using Sentinel-1 Synthetic Aperture Radar (SAR) Data

Flood has proven to be an incessant menace in Nigeria more threatening to riverine areas. The most recent flood ensued in 2022 as a result of heavy rainfall and the release of water from Lagdo Dam in Cameroon which became very devastating in many areas notably the Koton Karfe area in Kogi State, causing business shutdowns and the loss of lives and properties. In this work, Sentinel-1 Synthetic Aperture Radar (SAR) imagery was used for flood inundation mapping, and the accompanying damages were investigated using Landsat derived Land cover maps of Koton Karfe during the 2022 devastating flood. Overall, the results obtained in this study show that the regions that felt the impact of the flood the most were the southern and western areas, which must have experienced such an impact due to their proximity to the rivers Niger and Benue and also the water coming from the upper stream part of Cameroon. Further findings revealed that during the flood period on October 13, 2022, the total inundated area in Koton Karfe was estimated to be 198.255 sq. km. In terms of damage assessment, the urban areas had reduced from 220.902 sq. km in May 2022 to 87.473 sq. km in November 2022. This shows that over 133 sq. km of the urban settlement have been lost, indicating that lives must have been lost, properties too, and humans must have been displaced. This research will assist in the space of flood emergency response and disaster management.

Real-time peak flow prediction based on signal matching

Real-time peak flow prediction under heavy precipitation is critically important for flood emergency evacuation planning and management. In the case of emergency evacuation, every second matters as a slightly longer lead time could save more lives and reduce the associated social, economic, and health impacts. Here, we present a model (named SIGMA) based on the principle of signal matching to facilitate real-time peak flow prediction at sub-hourly scales (e.g., minutes to seconds). The SIGMA model divides the target watershed into small zones and the heavy precipitation falling into each zone is collected into a small water tank. As the water tank moves downstream and arrives in the watershed outlet, it will discharge the collected precipitation and generate a small single-pulse streamflow signal. By combining all small signals coming from all zones within the watershed, we will be able to generate a synthesized peak flow signal. The proposed model is applied to simulate the peak flow events observed in a real-world watershed to verify its effectiveness in real-time flood prediction. The results suggest that the presented model can reasonably predict three key aspects of a peak flow event, including the peak flow rate, the arrival time of peak flow, and the duration of the peak flow event. The proposed model is demonstrated to be effective in real-time flood prediction and can be used to support flood emergency evacuation planning and management.

A GPU-based hydrodynamic numerical model for urban rainstorm inundation simulations

Abstract The response capacities of urban flood forecasting and risk control can be improved by strengthening the computational abilities of urban flood numerical models. In this work, a GPU-based hydrodynamic model is developed to simulate urban rainstorm inundations. By simulating rainstorm floods in a certain area of Xixian New City, the established model can implement high-resolution urban rainstorm inundation simulations with significantly accelerated computing performances. The accelerated computation efficiencies of the different rainstorm event simulations under resolutions of 5 and 2 m are quantitatively analysed, showing that the absolute and relative speedup ratios for all scenarios of applying two GPUs range from 10.8 to 12.6 and 1.32 to 1.68 times as much as those of a CPU and a single GPU, respectively. The application of a large-scale rainstorm inundation simulation shows the excellent acceleration performance of the model compared to previous research. In addition, the greater the number of computational grids included in the simulation, the more significant the effect on the acceleration computing performance. The proposed model efficiently predicts the spatial variation in the inundation water depth. The simulation results provide guidance for urban rainstorm inundation management, and they improve the time and efficiency of urban flood emergency decision-making.

Strategic analysis on sizing of flooding valve for successful accident management of small modular reactor

In contrast to all-time flooded small modular reactor (SMR) systems, an in-kind flooding safety system (FSS) has been proposed as a passive safety system applicable to small modular reactors (SMRs) that adopt a metal containment vessel (MCV). Under transient conditions, the FSS can provide emergency cooling to dry reactor cavities and sustain long-term coolability using re-acquired evaporated steam in the reactor building on demand. When designing an FSS, the effect of the flooding flow area is vital as it affects the overall accident sequence and safety. Therefore, in this study, a MELCOR model of a reference SMR is developed and numerical analysis is performed under postulated accident scenarios. Without flooding, the MCV pressure of the reactor module exceeds the design pressure before core damage. To prevent core damage, an emergency flooding strategy is devised using various flow path parameters and requirements to ensure an adequate emergency coolant supply before the core damage is investigated. The results indicate that a flow area exceeding 0.02 m2 is required in the FSS to prevent MCV overpressure and core damage. This study is the first to report a strategic analysis for appropriately sizing an FSS flooding valve applicable to innovative SMRs.

A user experience evaluation of a mobile application for disseminating site‐specific impact‐based flood warnings: The A4alerts app

AbstractPeople‐centred early warning systems must ensure that their warnings are timely, actionable and get to the largest number of exposed individuals in the shortest time possible. Due to the increased access to high‐bandwidth communication, mobile applications have become a valuable complement to traditional sources of emergency information. However, there is limited research on how useful end‐users perceive these apps and their functionalities in the context of flood emergencies. This research presents the first online user experience evaluation of the A4alerts, a crisis app developed to disseminate the flood warnings triggered by the operational impact‐based early warning system in Catalonia, Spain. Following an interactive experiment using the A4alerts app to access emergency information within real‐based flood scenarios, participants were prompted to measure their experience using statements linked to criteria on usefulness, functional appropriateness, and adoption. The evaluation results suggest that sampled participants considered the A4alerts a useful communication tool to support end‐users and their emergency actions. In particular, the local impact‐based flood warnings, regional official warnings and self‐protection action checklist were ranked as the most useful functionalities implemented within the app. These findings justify future iterations of the A4alerts for supporting the rapid dissemination of emergency information and helping communities reduce the impacts of flood events.

SOSFloodFinder: A text-based priority classification system for enhanced decision-making in optimizing emergency flood response

<p>Flooding is a significant concern in nations with frequent precipitation because it can instantly affect multiple regions simultaneously. Due to the unpredictability of their occurrence caused by rapid water level rise, it is challenging to predict such natural disasters accurately. During flooding, prompt rescue efforts are crucial for the affected population. Due to flooded highways and residences, rescue teams may have difficulty locating victims. This hinders the potentially perilous and time-consuming rescue operation. To address this problem, we propose a web-based system that integrates natural language processing (NLP) with global positioning system (GPS) functionality. The SOSFloodFinder system provides automatic classification priorities for text messages sent by flood victims, as well as their most recent or current locations. The classification of text based on priority enables efficient resource allocation during rescue operations. In conclusion, this system has the potential to reduce future flood-related fatalities. Additional research and development are necessary to thoroughly investigate this method’s practical capabilities and effectiveness.</p>

Beyond the Embankments: Virtual Training on Human–River Interaction for Fostering Safer Behaviors during Flood Emergencies

Floods are an increasingly frequent phenomenon with catastrophic consequences for citizens and cities. Virtual reality (VR) has become a valuable training tool, simulating inaccessible or hazardous situations like emergencies and risk environments. This study evaluates a designed virtual training simulating a riverbank flood scenario. Beyond imparting factual knowledge, this environment aims to facilitate experiential learning by enabling users to repeatedly practice correct behaviors safely. We investigate the impact that different tutorials have on the ability of users to comprehend and retain the knowledge conveyed during the VR experience. To achieve this, three training experiences with different levels of contextualization and interaction tutorials were compared in a between-participant design. Data collection involved both self-reported measures (e.g., questionnaire) and objective measures (e.g., behavioral data). The results demonstrate the effectiveness of the virtual environment in acquiring knowledge of flood contents, particularly when the tutorial offers high levels of interaction, thereby reducing cognitive load. Furthermore, we observed that a lower level of contextualization in the tutorial phase enhances user efficiency in acquainting themselves with the virtual environment. This study emphasizes the potential of virtual training in augmenting preparedness, providing a valuable framework for the development of experiential learning tools in flood emergencies.