Flood Disaster Response Research Articles

2022 Flood Impact in Pakistan: Remote Sensing Assessment of Agricultural and Urban Damage

Pakistan was hit by the world's deadliest flood in June 2022, causing agriculture and infrastructure damage across the country. Remote sensing technology offers a cost-effective and efficient method for flood impact assessment. This study is aimed to assess the impact of flooding on crops and built-up areas. Landsat 9 imagery, European Space Agency-Land Use/Land Cover (ESA-LULC) and Soil Moisture Active Passive (SMAP) data are used to identify and quantify the extent of flood-affected areas, crop damage, and built-up area destruction. The findings indicate that Sindh, a province in Pakistan, suffered the most. This impact destroyed most Kharif season crops, typically cultivated from March to November. Using the SMAP satellite data, it is assessed that the high amount of soil moisture after flood also caused a significant delay in the cultivation of Rabi crops. The findings of this study provide valuable information for decision-makers and stakeholders involved in flood risk management and disaster response.

Exploring Generative AI for YOLO-Based Object Detection to Enhance Flood Disaster Response in Malaysia

Exploring Generative AI for YOLO-Based Object Detection to Enhance Flood Disaster Response in Malaysia

Eliciting social themes of flood mitigation and community engagement studies through text mining

PurposeFlood preparedness and response from the perspective of community engagement mechanisms have been studied in scholarly articles. However, the differences in flood mitigation may expose social and behavioural challenges to learn from. This study aimed to demonstrate how text mining can be applied in prioritising existing contexts in community-based and government flood mitigation and management strategies.Design/methodology/approachThis investigation mined the semantics researchers ascribed to flood disasters and community responses from 2001 to 2022 peer-reviewed publications. Text mining was used to derive frequently used terms from over 15 publications in the Scopus database and Google Scholar search engine after an initial output of 268 peer-reviewed publications. The text-mining process applied the topic modelling analyses on the 15 publications using the R studio application.FindingsTopic modelling applied through text mining clustered four (4) themes. The themes that emerged from the topic modelling process were building adaptation to flooding, climate change and resilient communities, urban infrastructure and community preparedness and research output for flood risk and community response. The themes were supported with geographical flood risk and community mitigation contexts from the USA, India and Nigeria to provide a broader perspective.Originality/valueThis study exposed the deficiency of “communication, teamwork, responsibility and lessons” as focal themes of flood disaster management and response research. The divergence in flood mitigation in developing nations as compared with developed nations can be bridged through improved government policies, technologies and community engagement.

Effects of Extreme Precipitation on Runoff and Sediment Yield in the Middle Reaches of the Yellow River

Understanding the link between extreme precipitation and changes in runoff and sediment yield is of great significance for regional flood disaster response and soil and water conservation decision-making. This study investigated the spatial and temporal distribution of extreme precipitation (characterized by 10 extreme precipitation indices recommended by the Expert Team on Climate Change Detection and Indices) in the Toudaoguai–Longmen section of the middle Yellow River from 1960 to 2021 and quantified the effects of extreme precipitation on runoff and sediment yield based on the method of partial least squares regression (PLSR). The extreme precipitation index showed an obvious upward trend in the last 20 years, with the increases in the central and northern regions (upstream) being stronger than the increase in the southern region (downstream). However, the runoff and sediment yield decreased significantly due to the implementation of large-scale soil and water conservation measures on the Loess Plateau, with average rates of 94.7 million m3/a and 13.3 million t/a during 1960–2021, respectively. The change points of runoff and sediment yield change occurred in 1979. Compared with those in the period from 1960 to 1979, the reductions in runoff and sediment yield in the years 1980–2021 were 52.7% and 70.6%, respectively. Moreover, extreme precipitation contributed 35.3% and 6.2% to the reduction in runoff in the 1980–1999 and 2000–2021 periods, respectively, and contributed 84.3% and 40.0% to the reduction in sediment yield, respectively. It indicated that other factors (such as large-scale soil and water conservation construction) played main roles in the decrease in runoff and sediment yield in the study area in recent 20 years.

Flood Disaster Response Cadre Training in Keliling Benteng Ulu Village, Martapura Barat Sub-District

A flood is an event where land is submerged due to an increase in water volume. The flood that occurred in RT.05 Keliling Benteng Ulu Village was the worst compared to other RTs, because they live on the edge of the Martapura river and swamp environment. This service activity educates cadres to fulfill basic needs (clean water and sanitation needs, food clothing, temporary shelter, and health services) and provides leaflets/booklets on the stages of flood disaster management in fulfilling the basic needs of the community. The results of the pre and post test evaluation, cadres' knowledge has increased ≥ 80% regarding flood disaster response and about fulfilling the basic needs of flood disaster management independently (making freshwater fish jerky and instant ginger).

An Ontology and a Reasoning Approach for Evacuation in Flood Disaster Response

Managing flood-related data to assist in the disaster management is a critical process of high importance during a flood disaster. These data are heterogeneous and can be provided from different data sources, and integrating them is a challenging task which allows inferring new information that helps in limiting the consequences of a flood. In this paper, we propose a novel approach that manages heterogeneous flood-related data based on semantic web techniques and helps in limiting the damage caused by floods. We first propose an ontology that is used to formally describe the flood-related data, and we construct our knowledge graph through integrating heterogeneous data using the proposed ontology. Then, we propose a reasoning approach using SHACL rules to infer new information that helps manage the flood disaster or anticipate future events. The experimental evaluations of our proposed approach are conducted on a real case study in flood disaster management with the aim of generating evacuation priorities. The results show that the proposed approach succeeds in managing heterogeneous flood-related data and in generating evacuation priorities in a very short time.

An ontological data model to support urban flood disaster response

This work aims to develop an ontological data model to support urban flood disaster response. The preliminary studies of the existing flood ontologies revealed that the developed ontologies do not express the relationships between the different levels of administration involved during flood disasters. So, an ontology named Flood Disaster Support Ontology (FDSO) was developed. The FDSO acts as a data model that can accommodate the various organisations and agents of different levels of the administrative agencies that are part of the flood disaster management process. It depicts their relationships and their activities specifically during an urban flood disaster response. The ontology also organises the resources and the information about them that are possessed by different organisations and may be used by first responders to carry out the activities such as rescue and relief. An amalgamation of two existing methodologies Yet Another Methodology for Ontology Development (YAMO) and NeOn was used to construct the ontology. The ontology was evaluated syntactically using reasoners along with the OOPS!, an OntOlogy pitfall scanner, whereas the semantic evaluation was done through SPARQL queries. The process yielded satisfactory results.

Improved Extreme Rainfall Frequency Analysis Using a Two‐Step Kappa Approach

AbstractAccurate estimation of annual exceedance probabilities (AEPs) of extreme rainfalls through rainfall frequency analysis is a critical step in engineering design for flood mitigation and disaster response. Here we show how the estimation of rainfall frequency curves can be improved by fitting a four‐parameter Kappa distribution to a peaks‐over‐threshold (POT) series. To fit the Kappa distribution to POT data we present a two‐step fitting approach based on maximum likelihood estimation which separately models storm intensity and the arrival frequency. First, a Generalized Pareto distribution (GPA) describing storm intensity is fitted, followed by a Binomial distribution for storm arrivals. We compare the performance of this two‐step Kappa approach to an analogous two‐step Generalized Extreme Value (GEV) approach and to Kappa and GEV distributions fitted to annual maxima series (AMS), using both synthetic and real‐world data representative of Australian climatic conditions. The experiments show that leveraging additional information from the POT series in the two‐step Kappa approach dramatically improves quantile estimation and reduces uncertainty compared to fitting either the GEV or the Kappa distributions to AMS, particularly for rare quantiles. When skew–kurtosis properties of extreme rainfalls are well represented by the Kappa but not the GEV, the two‐step Kappa approach yields unbiased quantiles under extrapolation, while the use of GEV can lead to highly biased estimates. From these results, we believe the two‐step Kappa approach is suitable for both at‐site and regional rainfall frequency analyses as it can accommodate distributions ranging from GPA to GEV without encountering over‐fitting problems generally associated with four‐parameter distributions.

Risk Narratives for Enhancing Regional Resilience: Constructing Evidence-Based Flood Disaster Response Scenarios

Literature exists on business continuity; however, little exists on the complied experience, especially flood risk. The research also does not cover industrial complex areas using integrated perspectives. Most studies on major business continuity disasters focus on event impacts and the short-term responses and recovery process of enterprises. Some evaluate the underlying causes of vulnerability, but few follow up to evaluate the consequences of the business continuity process because of restrictions on information disclosure regarding these activities. The objective of this study is to improve understanding of the influence that business continuity narratives have had on how decisions and actions are undertaken to continue business after a flood disaster, and what long-term influence this has had, in turn, on the industrial complex area from integrated perspectives, especially applying the lessons learned. This research drew on insights from in-depth studies of Japanese enterprises to maximize the findings based on abundant field data: (1) disaster responses in the flood risk situation; (2) the challenges faced by enterprises in the area before, during and following the 2011 floods; and (3) lessons that led to new consideration for the flood risk in the areas following the 2011 flood. This study identified alternative narratives on the purpose and means of business continuity with implications for flood risk by constructing scenarios for practical use. The findings of this study provide new insights and will improve the performance of business continuity management, both existing and planned, and, ultimately, support more climate-resilient development in this area.

Pattern of spatio-temporal variability of extreme precipitation and flood-waterlogging process in Hanjiang River basin

Based on the uniform distributed rainfall data in the Hanjiang River basin, the characteristics of spatio-temporal variability of extreme precipitation and flood-waterlogging process are diagnosed in terms of index definition and mathematical statistics. The study found that: (1) no significant trends of extreme rainfall for the entire basin were tested, but there was a jump variation in 1990s from abundance to dryness, accompanied by 20-year main periodic oscillation. Since 2000, the frequency of extremely heavy rain has increased significantly. (2) there is a spatial distribution pattern of lower in the northeast and higher in the southwest, and decreasing from upstream to downstream in the annual means of extreme precipitation. High-value centers are concentrated in the southwestern mountainous areas and the plains in the downstream, which are prone to form the distribution pattern of Central-North Radiation Type and Southeast-Northwest Type. (3) basin usually suffers 5–6 waterlogging processes per year, most of which were light or moderate grade, lasting 2–4 days. Since 2000, the flood-waterlogging process has shown evolution characteristics in increasing of the intensity, frequency, duration, and fluctuation, having strong continuity in the future. (4) affecting by climate and topography, spatial distribution of flood-waterlogging is uneven. Downstream and southwest of upstream are regularly the high-value centers, which are easy to form three kinds of distribution patterns, namely Northeast Radiation Type, Middle-lower Radiation Type and Middle-upper Radiation Type. Since the 21st century, the Hanzhong area and the watershed below Xiangyang both often encountered flood-waterlogging processes of high-intensity, long-duration, and high-frequency, which easy to induce flood disasters. Spatio-temporal variability of extreme precipitation and flood-waterlogging is a direct result of the rational development and utilization of water resources. We expect the results here to provide a deep insight into flood-waterlogging and serve as a reference for the watershed flood disaster response, rain-flood utilization, and optimal allocation of water resources.

Climatic and Hydrological Factors Affecting the Assessment of Flood Hazards and Resilience Using Modified UNDRR Indicators: Ayutthaya, Thailand

This research aims to investigate the effect of climatic and hydrological factors on flood hazards and assess flood resilience in Ayutthaya, Thailand, using the 10 essentials for making cities resilient modified by the United Nations Office for Disaster Risk Reduction (UNDRR). Flood resilience assessment was performed based on a multi-criteria decision-making approach or the analytical hierarchy process (AHP) of pairwise comparison. The results indicate that runoff is considered the most influential factor in flood hazards, followed by land use, rainfall, and historical flood events, sequentially. Regarding the flood incident management concept, a questionnaire survey (n = 552) was conducted to understand the impacts of flood on local communities. The findings reveal that 50% of respondents had never received any flood information or participated in training sessions on flood preparedness. Most reported their concerns about the inadequate supply of drinking water during a flood. Spearman’s correlation coefficient shows positive correlations between flood disaster relief payments, preparedness training, access to flood hazard mapping, emergency health services, and their flood preparation actions. According to the modified UNDRR indicators, the top three highest AHP values in building community resilience to flood hazards in Ayutthaya are flood risk scenario identification, the effectiveness of emergency flood disaster response, integrated urban planning, and disaster risk reduction. The policy implications of this research include the need for national authorities to better understand the role cities can play a vital role in supporting both national and international climate resilience frameworks, especially Thailand’s National Disaster Management Plan, the Sendai Framework for Disaster Risk Reduction (SFDRR), and the global Sustainable Development Goals (SDGs).

Evolution of potential evapotranspiration and its sensitivity to climate change based on the Thornthwaite, Hargreaves, and Penman–Monteith equation in environmental sensitive areas of China

Global warming intensifies the water cycle and regional water condition differences by changing the surface environment. The ecological system in environmental sensitive areas of China (ESAC) is stressed by water resources, and the ecological effect of dry–wet evolution is even more profound. However, the applicability of different potential evapotranspiration (ET0) assessment methods and the response mechanism of ET0 to climate change remain unknown. In this study, we evaluated the reliability of ETTH, ETHarg, and ETPM derived from Thornthwaite (TH), Hargreaves (Harg), and Penman–Monteith (PM) formulas using the monthly average air temperature (Tavg), maximum air temperature (Tmax), minimum air temperature, sunshine duration (SSD), wind speed (WIN), and relative humidity (RHU) from 1961 to 2018. Further, non-parametric trend analysis, correlation analysis, multiple regression analysis, wavelet analysis, and seasonal and trend decomposition using Loess were used to analyze the spatial–temporal evolution of ET0 and the moisture index (MI) and its relationship with climate change in ESAC from 1961 to 2018. The results are as follows: (1) The P-M formula has excellent applicability in the research area. Under the background of average temperature rising at 0.035 °C yr−1, ETPM decreases with a rate of −0.59 mm yr−1, which indicates the “pan evaporation paradox” phenomenon. Using 1993 as the node, ETPM changed from a significant decrease to an increase. (2) WIN, SSD, Tavg, and Tmax significantly promoted changes in ETPM, and RHU and precipitation (Pr) were mainly reduced. The determinant factor of ETPM change was Tavg from 1961 to 2018. (3) MI change was mainly influenced by Pr, SSD, and ETPM. Pr promoted the change, whereas SSD and ETPM reduced it. The linear combination of ETPM and SSD could explain the change in MI. Our findings verified the applicability of PM in areas with severe interannual precipitation fluctuations, revealing the evolution of dry–wet pattern changes and their driving mechanisms in ESAC, which can deepen the cognition of the law of long-term land surface water cycle and effectively guide the formulation and implementation of regional drought and flood disaster response measures.

The Role of Global Data Sets for Riverine Flood Risk Management at National Scales

AbstractOver the last two decades, several data sets have been developed to assess flood risk at the global scale. In recent years, some of these data sets have become detailed enough to be informative at national scales. The use of these data sets nationally could have enormous benefits in areas lacking existing flood risk information and allow better flood management decisions and disaster response. In this study, we evaluate the usefulness of global data for assessing flood risk in five countries: Colombia, England, Ethiopia, India, and Malaysia. National flood risk assessments are carried out for each of the five countries using six data sets of global flood hazard, seven data sets of global population, and three different methods for calculating vulnerability. We also conduct interviews with key water experts in each country to explore what capacity there is to use these global data sets nationally. We find that the data sets differ substantially at the national level, and this is reflected in the national flood risk estimates. While some global data sets could be of significant value for national flood risk management, others are either not detailed enough, or too outdated to be relevant at this scale. For the relevant global data sets to be used most effectively for national flood risk management, a country needs a functioning, institutional framework with capability to support their use and implementation.

Flood Extent Mapping During Hurricane Florence With Repeat‐Pass L‐Band UAVSAR Images

AbstractExtreme precipitation events are intensifying due to a warming climate, which, in some cases, is leading to increases in flooding. Detection of flood extent is essential for flood disaster response, management, and prevention. However, it is challenging to delineate inundated areas through most publicly available optical and short‐wavelength radar data, as neither can “see” through dense forest canopies. In 2018, Hurricane Florence produced heavy rainfall and subsequent record‐setting riverine flooding in North Carolina, USA. NASA/JPL collected daily high‐resolution full‐polarized L‐band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data between September 18th and 23rd. Here, we use UAVSAR data to construct a flood inundation detection framework through a combination of polarimetric decomposition methods and a Random Forest classifier. Validation of the established models with compiled ground references shows that the incorporation of linear polarizations with polarimetric decomposition and terrain variables significantly enhances the accuracy of inundation classification, and the Kappa statistic increases to 91.4% from 64.3% with linear polarizations alone. We show that floods receded faster near the upper reaches of the Neuse, Cape Fear, and Lumbee Rivers. Meanwhile, along the flat terrain close to the lower reaches of the Cape Fear River, the flood wave traveled downstream during the observation period, resulting in the flood extent expanding 16.1% during the observation period. In addition to revealing flood inundation changes spatially, flood maps such as those produced here have great potential for assessing flood damages, supporting disaster relief, and assisting hydrodynamic modeling to achieve flood‐resilience goals.

Factors affecting perceived effectiveness of Typhoon Vamco (Ulysses) flood disaster response among Filipinos in Luzon, Philippines: An integration of protection motivation theory and extended theory of planned behavior

Typhoon Vamco (Ulysses) is considered one of the most devastating typhoons in the Philippines in 2020. It caused fatalities, property destructions, and catastrophic flooding in Metro Manila and the nearby provinces. The purpose of this study was to assess the factors affecting the perceived effectiveness of the 2020 typhoon Vamco (Ulysses) flood disaster response among Filipinos by integrating the Protection Motivation Theory (PMT) and extended Theory of Planned Behavior (TPB). 567 Filipinos answered an online survey questionnaire with 75 questions (63 indicators and 11 latent variables). Structural equation modeling (SEM) showed that geographical perspective (GL) and typhoon – flood experience and knowledge (TPE) had significant effects on Perceived Severity (PS) and Perceived Vulnerability (PV), which subsequently led to Perceived Behavioral Control (PBC), Subjective Norms (SN), and Attitude (ATB). PBC, SN, and ATB were also found to have significant effects on Intention to Follow, which subsequently led to Behavior and Perceived Effectiveness from the 2020 Typhoon Vamco (Ulysses) flood disaster risk response. Interestingly, government support (GR) was insignificant to an effective flood disaster response, and 52.38% of respondents perceived that the typhoon had more severe effects than the flood after the typhoon. This study is the first study that analyzed the perceived effectiveness of disaster response towards 2020 Typhoon Vamco. The findings will be very beneficial for academicians and policymakers as it provides a robust model and results for experts to analyze natural disasters to develop optimum disaster risk responses in mitigating the severe effects of typhoon floods. Finally, the SEM construct can be broadened and adapted to flood disaster response effectiveness applicable in other natural disaster-prone countries.

How Far Disaster Management Implemented Toward Flood Preparedness: A Lesson Learn from Youth Participation Assessment in Indonesia

Flood is a common and frequent natural disaster in many countries that causes huge economic losses and casualties every year. Youth participation in flood disaster management (FDM) has not been much explored, especially in the non-prone area but contributing to flooding resilience. Therefore, this study aims to identify youth participation in disaster management to help an improvement in preparedness action. The research was conducted using a qualitative model: case study research, involving 191 young people aged 14-35-years in 16 sub-districts in Semarang City. The data, including youth’s action, knowledge, and participation in FDM, was collected using Google Form, observation, and interview, then statistically analyzed using Mann-Whitney’s test and path analysis. The results show the respondents in flood-affected areas are more actively participating in flood disaster management action because of their experience in facing flooding. Also, the planning step is significantly influenced by the FDM implementation. The planning process is the main defining factor in disaster management successfulness and essentially affecting mitigation, rehabilitation, and evaluation steps. The level of youth participation is deemed necessary to be increased to develop a more comprehensive disaster management program according to regional needs. We suggest that FDM should be transformed into disaster awareness which is delivered through education, socialization, training, and/or flood disaster response simulations.

Development of a method for flood detection based on Sentinel‐1 images and classifier algorithms

AbstractFloods are one of the most devastating natural disasters in the world, displacing millions of people each year and causing severe damage to people's lives and infrastructure. It is the most widespread hydrological hazard worldwide that affects water management, nature protection, economic activities, hydromorphological alterations on ecosystem services and human health. Real‐time monitoring systems play a key role in flood risk reduction and disaster response decisions. Studies have shown that using earth observation data for flood monitoring and timely actions based on good quality information reduces damages. In this paper, after thresholding, a machine learning algorithm and an object‐based classification method were used to classify the SAR data. Thresholding helps detect regions in the flooded areas. A comparison of the results showed that the machine learning algorithm obtained significant results. Because of the results obtained, the usefulness of Sentinel‐1 images as a baseline data for the improvement of the methodological guide is appreciated and should be used as a new source to monitor the flood risks.

Evaluating the Influence of Resident Agencies’ Participation in Flood Management via Social Media, in Nigeria

Disaster management is conducted in multilevel and different stages. However, these different stages are tied together by the key infrastructural components of Information and Communication Technology (ICT) such as social media which is central to the effectual management of disaster through residents’ participation. Nevertheless, there has been limited access to information technologies that enhance quick recovery services, timely response, and effective dissemination of disaster-related information. Therefore, this study examined the influence of resident agencies’ participation in disaster management via the use of social media information planning and training possibilities (SMDPT) in Nigeria. The study used a survey questionnaire and stratified random sampling technique to collect 370 primary data from flood disaster management agencies in Ibadan, Nigeria. The collected data were analyzed using Partial Least Square - Structural Equation Modeling (PLS-SEM). The study found that social media information planning and training (SMDPT) had a positive influence on flood disaster preparedness (DPRE), disaster response (DRES), and disaster recovery (DREC). Therefore, the study concludes that social media has become an important tool for planning during an emergency situation. Hence, an important implication of this study to the environmental regulatory policymakers and emergency response agencies is to create more awareness on the usability and applicability of social media in providing credible information regarding disaster management.

Optimization model for temporary depot problem in flood disaster response

The objective of this research is to establish a model that characterizes integrated humanitarian operations management in response to flood disasters, during which an optimal framework that describes the interactions between different elements in the relief supply chain is crucial. In principle, it is necessary to develop a model that controls the total flow of supply distribution and evacuation planning, as well as the location routing of facilities that handle relief supplies from the sources all the way down the relief chain to the hands of the recipients. The location-routing model was proposed in response to the temporary depot problem that is particular to such disasters. To formulate this problem, the use of a multi-period approach to describe the problem was presented, and multimodal transportation was considered in order to more closely mimic the realistic behavior of a flooding disaster.

Community Based Flood Catastrophe Preparedness, Vulnerability, Management and Response of Layyah District, Punjab, Pakistan (1990-2015)

The study articulates the society supported flood disaster preparedness, vulnerability, management, and response in the Layyah district, southern Punjab, Pakistan. The area has recorded high summer temperature, low rainfall and an arid climate with an optimistic departure of one centimeter. At Taunsa barrage, the Indus river, as well as the Thal canal, show a decline in the annual flow, while it remains high at Chashma barrage having a heavy flow from July to October. Historically, the foremost disasters experienced in Layyah district are floods, earthquakes, droughts, epidemics, and fires. About 81 Potwar areas have been affected every year by flood disaster consisting of 36 medium and 45 high risks units. Obviously, the locals are the primary instrument to fight any catastrophe for their safety at the initial stage as they are admirable arbitrators of risk assessment, vulnerability, rehabilitation, excavation, and can acquire consistent estimations for their safety. The joint endeavors of the locals and government during predisaster and a post-disaster stage will make certain that how to provide crucial support as well as services to the distressed community throughout the Layyah district. The social or anthropogenic level arrangements for flood catastrophes are appreciated mutually at national and international levels.