Flood Emergency Response Research Articles

Flood simulation using LISFLOOD and inundation effects: A case study of Typhoon In-Fa in Shanghai

Urban flooding threatens residents and their property, necessitating timely and accurate flood simulations to enhance prevention measures. However, as a megacity, Shanghai presents a complex underlying surface that proves challenging to assess accurately in existing studies. To simulate the dynamic flooding caused by Typhoon In-Fa in Shanghai from July 23rd to 28th 2021, we employed the LISFLOOD hydrodynamic model with multi-source data and validated the flooded area using the S1FLOOD deep learning model with Sentinel-1 satellite imagery. Based on simulated flood results and a flood depth classification system, we quantified the impacts of flood inundation on population, land use, and buildings. Key findings include: (1) The most severe flooding period in Shanghai occurred on July 25th and 26th 2021. (2) The LISFLOOD model effectively captured the extent of inundation, with the very-high flood depth zone covering 98.07 % of the area identified as flooded by the S1FLOOD and Sentinel-1. (3) Peak-affected individuals were recorded on July 25th 2021. (4) Farmland experienced the most extensive flooding among land use types, while residential buildings were notably affected among building types. Our study reconstructed the spatiotemporal dynamics of Typhoon In-Fa-induced flooding in Shanghai. We mapped the spatial extent and water depths, revealing the dynamic impacts of inundation on population, land use, and buildings across urban areas. This comprehensive framework for flood simulation and inundation impact analysis offers a valuable approach to improve urban flood emergency response.

The status of response to the flood early warnings in Kilosa District, Tanzania

Floods have been a challenging phenomenon, jeopardizing human life and property. Measures have been taken to alleviate flood effects, including using flood early warning systems (EWS), which give warnings to people. Despite the use of warnings from EWS, there is limited information on warnings contribution to mitigate flood effects. This study investigates the status of flood early warning response measures by communities in areas where floods frequently occur in Kilosa District. The data were collected from 375 household heads through surveys and triangulated by 17 participants via focus group discussions, 22 officials through key informant interviews, and a literature survey. The data were descriptively analysed, and chi-square tests and binary logistic regression analyses were done. Results indicate that 76.53 % of the respondents receive flood early warnings from word of mouth among relatives (54.21 %), phones (45.45 %), and radios (31.65 %). Upon receiving the early warnings, 45.43 % inform family members about what to do, 43.82 % vacate flood-prone areas, and 6.99 % make flood emergency response plans. Those flood warning response measures are significantly an outcome of the households’ distance from the rivers causing floods (p < .0001), chances of receiving flood warnings (p < .0001), past flood experience (p < .0001), and the nature of the houses (p < .0007). In conclusion, individuals can proactively protect against floods by diverting floodwaters away from populated areas and reducing human exposure to flood risks. To actively protect against floods, communities should implement multiple warning systems, establish reliable mechanisms to disseminate alerts and install water level gauges in all flood-prone rivers.

The Relationship Between Health Workers' Knowledge And Flood Emergency Response Preparedness

Data from the National Disaster Management Agency (BNPB) reveals that the most common type of disaster is flooding. Based on the 2010-2030 Regional Spatial Plan (RT/RW) document of Padang City, as stipulated in Perda No. 03 of 2019, Article 58 B, there are 10 sub-districts in Padang City that are classified as flood-prone. These sub-districts include Koto Tangah, Kuranji, Pauh, West Padang, East Padang, South Padang, Lubuk Begalung, Nanggalo, Lubuk Kilangan, and Bungus Teluk Kabung. In this context, the preparedness of health workers in facing disasters is very important. This study aimed to assess the relationship between the level of knowledge of health workers and their preparedness in facing floods at Puskesmas Pauh, Padang City. The study was conducted in March 2024 involving 74 respondents as samples. Data were analyzed using the Chi-Square test with a 95% confidence level. Based on the characteristics of the respondents, the majority were between 37 to 47 years old (42 people or 56.8%), had a DIII education (47 people or 63.5%), were female (67 people or 90.5%), and worked as midwifery health workers (38 people or 51.4%). Most of the respondents had good knowledge related to flood disaster preparedness (52.7%) and had a mature emergency response plan (89.2%). The results of this study indicate a significant relationship between the level of knowledge of health workers and flood emergency response preparedness at Puskesmas Pauh, Padang City, with a p-value of 0.006.

Knowledge Management Model for Urban Flood Emergency Response Based on Multimodal Knowledge Graphs

Recently, frequent flood disasters in China have seriously threatened economic development and public safety. This paper addresses the need for a dynamic urban flood emergency knowledge management system in emergency management departments and the lack of systematic knowledge among emergency managers regarding urban flood control. A multimodal knowledge graph-based urban flood emergency knowledge management model was constructed to enhance the decision-making capabilities of emergency management departments, improve the efficiency of public emergency evacuation, and reduce losses from urban flood disasters by analyzing the shortcomings of the existing emergency management system. An intelligent and dynamic flood emergency knowledge management model was built. This paper integrates multimodal knowledge graph technology to establish a multimodal emergency knowledge management framework for urban flood control. It develops and simulates the proposed model’s application scenarios for urban flood emergency evacuation using the Flocking algorithm on the NetLogo platform. Through simulation experiments, the practicality and effectiveness of the model in real flood disaster situations were examined, particularly in simulating crowd evacuation behavior. The study found that the model significantly improves the accuracy of information and decision-making speed during emergency responses and supports emergency management departments in conducting targeted and personalized emergency decisions. This research provides a scientific basis for emergency management departments to optimize their emergency response strategies to flood disasters and serves as a reference and example for the application of multimodal knowledge graph technology in emergency management.

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.

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.

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.

Spatiotemporal Information Mining for Emergency Response of Urban Flood Based on Social Media and Remote Sensing Data

The emergency response is crucial in preventing and mitigating urban floods. Both remote sensing and social media data offer distinct advantages in large-scale flood monitoring and near-real-time flood monitoring. However, current research lacks a thorough exploration of the application of social media data and remote sensing imagery in the urban flood emergency response. To address this issue, this paper, while extracting disaster information based on social media data, deeply mines the spatiotemporal distribution characteristics and dynamic spatial accessibility of rescue points. Furthermore, SAR imagery and social media data for monitoring urban flooding are compared. This study took the Zhengzhou 7.20 urban flood as a case study and created a methodological framework to quickly extract flood disaster information (flood, landslide, and rescue points) using these two types of data; spatiotemporal analysis and random forest classification were also conducted to mine valuable information. Temporally, the study revealed that disaster information did not increase proportionally with the amount of rainfall during the rainfall process. Spatially, specific regions with higher susceptibility to flooding, landslides, and rescue points were identified, such as the central region characterized by low drainage standards and high-density urban areas, as well as the eastern region with low-lying terrain. Moreover, this study examined the spatial accessibility of rescue resources in real flood scenarios and found that their service coverage varied throughout the day during and after the disaster. In addition, social media excelled in high-density urban areas’ flood point extraction, while SAR performed better in monitoring floods at the edges of low-density urban areas and large water bodies, allowing them to complement each other, to a certain extent. The findings of this study provide scientific reference value for the optimal selection of rescue paths and the allocation of resources in the emergency response to urban floods caused by extreme rainstorms.

Implementasi Kebijakan Penanggulangan Bencana dan Mitigasi Banjir di Kabupaten Gresik

The flood disaster due to high rainfall and the overflow of the Lamong River which could not accommodate the incoming water discharge resulted in waterlogging in parts of Gresik Regency, especially those that occurred in Balongpanggang, Benjeng, Cerme, Menganti, and Driyorejo Districts. district. Knowing this, the Gresik Regency Government through the BPBD has made various efforts to minimize the impact that will be caused and optimize disaster management. The purpose of this research is to find out how the function of the Gresik Regency Regional Disaster Management Agency is implemented in flood emergency response. The method used in this research is descriptive qualitative with an in-depth approach. In this research using Policy Implementation Theory according to Edward III. The data used in this study were obtained from primary data and secondary data. The results showed that according to Edward III's theory, the implementation of the functions of the Gresik Regency Disaster Management Agency in disaster emergency response had been carried out properly and was also guided by Regional Regulation Number 7 of 2012 concerning Disaster Management.

Semantic segmentation of water bodies in very high-resolution satellite and aerial images

This study evaluates the performance of convolutional neural networks for semantic segmentation of water bodies in very high-resolution satellite and aerial images from multiple sensors with particular focus on flood emergency response applications. Different model architectures (U-Net and DeepLab-V3+) are combined with encoder backbones (MobileNet-V3, ResNet-50 and EfficientNet-B4) and tested for their ability to delineate inundated areas under varying environmental conditions and data availability scenarios. An unprecedented reference dataset of 1120 globally sampled images with quality checked binary water masks is introduced and used to train, validate and test the models for water body segmentation. Furthermore, independent test datasets are developed to test the generalization ability of the trained models across regions, sensors (IKONOS, GeoEye-1, WorldView-2, WorldView-3 and four different airborne camera systems) and tasks (normal water and flood water segmentation). Results indicate that across all tested scenarios a U-Net model with Mobilenet-V3 backbone pre-trained on ImageNet performs best. While using R-G-B image bands performs well, adding the near infrared band (if available) slightly improves prediction results. Similarly, adding slope information from an independent digital elevation model increases accuracies. Train-time augmentation and contrast enhancement could improve transferability across sensors and in particular between satellite and aerial images. Moreover, adding noisy training data from freely available online resources could further improve performance with minimal annotation effort.

Implementation of Flood Emergency Response System with Face Analytics

Disaster management system is developed to monitor flood, tsunami and earthquake, which is effectively preparing for and responding to the disaster. In fact, Malaysia is building the emergency response system for managing the flood disaster since it is highly occurred. However, the current flood emergency response system has limitations, which, has no integration that data entered into spreadsheets and transferred to different storages, and, data analytics that assist in data collection and decision-making. Even though flood emergency response system have been improved, which introducing sirens and loudspeakers to reach flood victims but still difficult to access the basic facilities and receive flood responses. Therefore, this study implements a flood emergency response system with face analytics to assist data acquisition, which analyze flood victim’s faces using CCTV infrastructure with HOG algorithm that incorporated with a dashboard. The dashboard categorizes the number of flood occurrence, maximum flood period (days), the number of displaced flood victims and loss assessment. Findings have shown that the dashboard helps the enforcement agencies to implement the real-time information about flood victims. Based on the number flood frequency occurrence in Malaysia from 2017 to 2021, the percentage produced was 27%, 19%, 12%, 19% and 23%. Moreover, the duration of floods has been decreased from 30% to 17% in 5 years that shows the flood emergency response helps the Malaysia government to improve the infrastructure. The significant of this study beneficial to the local enforcement unit and evacuation centers in identifying the flood victims.

Rising Water, Rising Voices: Empowering Communities Through The Flood Emergency Response Plan

Rising Water, Rising Voices: Empowering Communities Through The Flood Emergency Response Plan

Monitoring the Catastrophic Flood With GRACE-FO and Near-Real-Time Precipitation Data in Northern Henan Province of China in July 2021

Zhengzhou and its surrounding areas, located in northern Henan Province, China, receive continuous extreme rainfall from July 17 to July 22, 2021. Northern Henan Province experiences extensive flash floods and urban floods, causing severe casualties and property damage. Understanding the variation of hydrologic features during this flood event could be valuable for future flood emergency response work and flood risk management. This study first demonstrates the rainstorm process based on near-real-time precipitation data from the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0). To meet the temporal resolution required for monitoring this short-term flood event, reconstructed daily terrestrial water storage anomalies (TWSAs) based on GRACE and GRACE-FO data and CLDAS-V2.0 datasets are first introduced. The spatial and temporal evolution of the reconstructed daily TWSA is analyzed in the study area during this heavy rainfall event. We further employ a wetness index based on the reconstructed daily TWSA for flood warnings. Furthermore, the modeled soil moisture data and daily runoff data are used for flood monitoring. Results show that the reconstructed daily TWSA increases by 437.7 mm in just six days (from July 17 to July 22, 2021), with a terrestrial water storage increment of 9.4 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . Compared with ITSG-Grace2018, the reconstructed daily TWSA has better potential for near-real-time flood monitoring for short-term events in a small region. The wetness index derived from reconstructed daily TWSA is potential for flood early warning.

Evaluation of Emergency Response Capacity of Urban Pluvial Flooding Public Service Based on Scenario Simulation.

The evaluation of emergency response capability under different pluvial flooding scenarios is an essential approach to improve the emergency response capability of flood disasters. A new evaluation method of emergency response capacity of urban public services is proposed based on urban pluvial flooding scenario simulation. Firstly, inundation area and depth under different pluvial flooding scenarios are simulated based on the SCS-CN model. Following that, space densities of all indicators include inundation area and depth, road network and the emergency public service institutions. Then, the indicator weight is determined by the combined weighting method of entropy weight and coefficient of variation. Finally, the emergency response capacity index (of each pixel) is calculated based on the graph stacking method. Taking Erqi District, Zhengzhou City as an example, the emergency response capacity of public service under different urban flooding scenarios is evaluated. The results show that the spatial distribution difference of public service emergency response capacity in Erqi District, Zhengzhou City is obvious, and with the increase of the precipitation return period, the high value area of public service emergency response capability decreases gradually and the low value area increases gradually. This method takes into account the specific urban flooding scenario and the layout of public service institutions and road networks that have strong practicability. the results of the evaluation can provide a reference for the construction of urban flood emergency response capacity and provide support for emergency decision-making.

Sintang Regency flood analysis of 2021

A flood is a hydrometeorological disaster caused by several factors simultaneously. This paper aims to analyze the flood that hit the Sintang Regency in 2021. We analyze the flood spatially according to the method formulated by Watershed Management and Technology Center (WMTC), Surakarta. Sintang Regency grows at the Y-junction of two major rivers in the Kapuas Watershed, namely the Kapuas and Melawi River. The biophysical conditions in the upstream area affect the water level at the confluence. The results showed that 50% of the upstream Kapuas watershed is covered by forest. About 51% of land typology is vulnerable and the daily rainfall in October reached 138 mm, exacerbating the flooding in 2021. The flood emergency response status applies from October 21 to November 30, 2021. The floods inundated 12 of 14 districts in the Sintang Regency. Residents affected by flooding are caused by the location of their settlements on the riverside – the floodplain. One of the adaptations to the annual flooding carried out by the local community is to build houses on stilts. In addition, there is also lanting – a typical floating house. The location of lanting will adjust to the water level of the river.

THE EFFECT OF PREPAREDNESS MEASURES ON COMMUNITY PARTICIPATION IN FLOOD EMERGENCY RESPONSE IN RANGKASBITUNG DISTRICT

Every disaster brings victims, both human and property is a fact. But whatever the type of disaster, there is always an omen before it comes. This is where the urgency is to understand every sign that comes correctly and accurately. So, of course, knowledge, skills, and skills are needed on how the community, especially in disaster-prone areas, prepares anticipatory steps for the arrival of the disaster (Priyowidodo and Luik 2013). The readiness and skills of the community, especially families, are the primary keys to safety in dealing with disaster emergencies (Willem Rampangilei 2018). This study aims to determine the effect of preparedness measures on community participation in flood emergency response. This research uses research that uses correlational survey method. In this study, the independent variable is community participation in disaster emergency response, while the dependent variable is preparedness measures. This study aimed to determine the effect of preparedness measures on community participation in flood emergency response. The results of the survey: Preparedness measures (X) affect the community participation variable (Y) by 16%.

Towards the coordinated and fit-for-purpose deployment of Unmanned Aerial Systems (UASs) for flood risk management in England

Abstract Preparedness for flood emergency response is crucial for effective flood management. The need for advanced flood decision support tools that aid flood management has been recognized by several authors. This work examines the variability that currently exists across England with regard to the Unmanned Aerial System (UAS) data collection and processing strategy in flood emergency events. Expert elicitation was carried out using a tailored questionnaire about UAS deployment in three flood emergency scenarios. The survey highlighted that reduced equipment assembly time, a national network of appropriately qualified UAS pilots and the effective UAS deployment when on-site, can reduce the response time to flood emergency. For improved comparability and reduced bias in data collection and interpretation, clear guidelines on which data products are most beneficial for particular purposes, processing time required, platform and sensor selection may also be necessary. We consider that releasing a comprehensive documentation pack, which includes guidelines, standards and protocols that detail the methods, tools, technology, quantity and quality of data, to UAS pilots on a flood emergency call, will enhance the timely response.

Family preparedness of children in dealing with a flood disaster in Paupanda II Village, Wewaria sub-district of Ende Regency Indonesia

Background: For almost ten years Wewaria Village, Wewaria sub-district of Ende Regency suffered a flood disaster. The flood occurred in 2008, 2012, and 2016. Paupanda II Village has a puddle up to one and a half meter every year. Flood emergencies can cause family panic in rescuing themselves or their children under five. Children are a vulnerable group in the health sector because they cannot help themselves, when floods. If family preparedness is low, then the child’s health threatened, increasing the child’s morbidity or mortality rate. Purpose: This study explains or explore the experience of family preparedness who have children under five dealing with floods. Method: it used Purposive sampling in selecting the first informant then using snow ball sampling for the next informant. The number of samples is 9 people taken from a family of 6 people, 2 policy stakeholders, and 1 health worker. The qualitative analysis used is triangulation of data sources. Results: Family knowledge is well, but preparedness is still low for children needs, flood early warning, flood emergency response preparedness, and resource mobilization deal with floods.

Optimal Deployment of Flood Emergency Response Materials under Stochastic Demands

This study aims to develop a planning model for the deployment of flood emergency response materials under stochastic demands. For ease of modeling, a deterministic demand model is first proposed, from which the stochastic model is further extended. These models are used to determine the allocation of materials the transport truck routes and distribution of the stockpiled materials to emergency sites. The models are formulated as multiple integer network flow problems with special side constraints which are characterized as NP-hard in terms of optimization. To solve the complicated stochastic model efficiently, we also develop a solution algorithm based on a problem decomposition technique coupled with a variable fixing technique. Finally, to preliminarily evaluate the proposed models and solution algorithm, we perform numerical tests using data related to a Taiwan flood emergency response system. The test results are good, indicating the potential usefulness of the models and solution algorithm in practice.