research proposal on floods

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(Proposal) THE IMPACT OF FLOODS ON THE SOCIO-ECONOMIC LIVELIHOOD OF THE PEOPLE OF KWAPROW.

The frequency of natural disasters has been increasing over the years, resulting in loss of lives, damage to properties and destruction of the environment. The number of people at risk has been growing each year and the majority are in developing countries with high poverty levels making them more vulnerable to disasters. National governments can mitigate natural disasters if sound environmental performance are integrated into their daily activities.

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lickson L S mchepa

ABSTRACT Flash floods are common features in Malawi almost every rainy season, but the flood events of December2014 - March 2015 in Malawi which can be described as the worst in 20 years gave rise to situations where rivers overflowed their banks and submerged hundreds of kilometers of urban and rural lands. Flood disasters are major threats to human-beings and reverse major developmental processes in any locality, hampering socio/economic activities. Most of the rural areas affected by this flood have high poverty levels and many Female headed households. The poverty level affects the resilience and process of recovery from the flood disaster especially for the Female Headed Households as they are more vulnerable to flood impacts. This paper sought to communicate the impact of the floods on the socio-economic status of livelihood and the coping strategies of female headed households in Lower Shire districts of Chikwawa and Nsanje. Utilizing structured questionnaires and focus group discussions/interviews the study identified that the major livelihood of sampled female Headed households were crop production. 71.3% of the female headed households had their crops damaged and mainly the staple crops thus increasing food insecurity for the households. Their incomes were affected significantly as income source is imbedded in livelihood. This reduction in income also increased their vulnerability. Following fertile soils, lack of alternative livelihoods, lack of decision making power and poverty were identified as being the main underlying causes of vulnerability for the women in Lower Shire. The study identified that the coping strategies employed by female headed households were majorly a function of four factors namely: cultivating on small portions on higher grounds, temporal migration to higher grounds and raising the floor of the house. The current coping strategies being employed by female headed households are ineffective. There is a dire need for the female headed households to be given priority in times of aids provision during and after any natural disaster. Efforts are to be made by the communities at formulating sustainable mitigation measures in order to enhance community resilience in view of frequency/magnitude of floods experienced. Adequate funding towards risk mapping, monitoring and implementation of preparedness/mitigation measures should be implemented. Keywords: Flood disaster, Female headed households, Food security, Vulnerability, Resilience, Coping strategies

Imam Abd Sajid

Nitike Ngwira

Patrick Mashapa

Ernest Dube

AKINTOYE OLUYEMI AYORINDE, Univ. of Ife Dipl.Agric , BSc./MSc (Unical) PhD (Nigeria),PGD Missiology

The main purpose of this study is to identify the socioeconomic implications of recurrent flooding on women development in southern Ijaw Local Government Area. Generally, flooding may result in socioeconomic , ecological and health problems. This study assumes that on flood days the movement of customers and sellers tends to be hindered, thus resulting in the retardation of transactions and the reduction of daily income earned. The study compared the situation of female traders with that of male traders. Both primary and secondary data were used in this study. Primary data were collected using an open-ended questionnaire. A total of 83 questionnaires were randomly distributed to members of four communities, which were selected through stratified random sampling procedures. Also 33 randomly selected women and men respectively, engaged in marketing activities from open and locked-up shops, were sampled to observe the level of their personal income (in Naira), from customers patronage during 3 flood days and 3 non-flood days. Other data and information were obtained through Key Informants Interview (KII), and observations. Hypotheses I and II were tested using Analysis of Variance (ANOVA) statistical model. Null hypothesis I (H0), which states that " There is no statistically significant difference in the income earned by men and women traders from marketing activities on flood days and non-flood days in Southern Ijaw Local Government Area, Bayelsa State " , is accepted (F-value: 3.8723939, P-value: 2.494E−05), whereas null hypothesis II (H0), which states that " There is no statistically significant difference in the income earned by women traders from marketing activities on flood and non-flood days in Southern Ijaw Local Government Area, Bayelsa State, is rejected (F-value: 2.524902, P-value: 0.030069). Thus while there is no significant difference in the earnings of male O. A. Akintoye et al. 34 and female traders on flood and non-flood days, there are significant differences in sales earning among women traders on those days. Factors affecting trading income on flood and non-flood days include accessibility to business premises by customers, ability of male marketers to afford non-easily flooded business premises; and women traders with limited resources often have less suitably drained premises. Reduced total household income can detrimentally affect food affordability, availability, household nutrition, family health and wellbeing. Recommendations highlighting the roles of communities, government and stakeholders in flood management are proffered.

Dr. William Aduah Yorose

David A Ayariga

Ameen Benjamin

Focusing on South Africa, it becomes evident that traditional physical science approaches to flood risk management is not adequate for a comprehensive understanding of the flood risk of peri-urban communities. A more integrated approach that draws methods from both the physical and social sciences becomes necessary to better understand the physical flood hazard(scape) and the vulnerability of the at-risk population. Such an understanding is anticipated to lead to a more comprehensive strategy for urban flood risk management in the developing country context. This book is an attempt to demonstrate, by way of actual events, how such an integrated approach can be achieved and the merits thereof. The research is based on field experience of the 2006 and 2007 extreme weather events in the town of George in the Western Cape Province of South Africa.

Flood has for the past few years become a ‘normal’ phenomenon at Atonsu. The town has often been subjected to periodic occurrences of flooding lasting averagely a week on each incident. This phenomenon has attracted little attention from local government and other key stakeholders such as the central government. The study assessed the effectiveness of land use planning, planners’ roles and public participation in addressing the situation. It also considered into details, the causes and effects of flooding to residents in the various suburbs surveyed and their adaptation mechanisms before, during and after each flood incident. A mixed research design was used in generating data for analysis. Qualitative data regarding land use planning, the planning process and planners’ roles was obtained from institutions such as the Metropolitan Unit of Town and Country Planning Department (TCPD) and Kumasi Metropolitan Assembly (KMA) using interviews. Others, such as institutional support mechanisms for flood victims were obtained from National Disaster Management Organisation (NADMO). A survey of residents of flood prone suburbs was conducted using pre-coded and open-ended questionnaires to assess the causes, effects and adaptation mechanisms by residents towards flooding. The results revealed that, among other causes, building on the floodplain and the absences of drains impede the free flow of excess water during each downpour. It was equally observed that the existence of a planning scheme had little influence on the leap-frog developments springing up. The common adaptation employed by residents was structural; building walls around entrances as well as building on stilts. The study strongly recommends that, the Metropolitan Assembly takes immediate steps to dredge the Ahinsan stream that traverses the town so as to increase the free flow of excess water. It is also suggested that, land use planning and planners’ roles should be more defined and enhanced by the Assembly to enable them proactively handle the situation.

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A review of the flood management: from flood control to flood resilience

Lihong wang.

a Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China

b University of Chinese Academy of Sciences, Beijing 100049, Xiamen Key Lab of Urban Metabolism, Xiamen 361021, China

c Xiamen Key Lab of Urban Metabolism, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China

Shenghui Cui

Yuanzheng li.

d School of Resources and Environment, Henan University of Economics and Law, Zhengzhou 450046, China

Hongjie Huang

Bikram manandhar.

f Tribhuvan University, Institute of Forestry, Hetauda 44107, Nepal

Vilas Nitivattananon

e Urban Environm Management, Asian Institute of Technology, Pathumthani 12120

Xuejuan Fang

Associated data.

No data was used for the research described in the article.

Climate change and socioeconomic developments are increasing the frequency and severity of floods. Flood management is widely recognized as an effective way to reduce the adverse consequences, and a more resilient and sustainable flood management approach has been the goal in recent studies. This study used a detailed bibliometric analysis of keywords, terms and timelines in the research field of the flood research. It provides new insight into the flood research trends, by examining the research frontiers from 2000 to 2021. We conclude that the trend of flood research has experienced a transition from flood control to flood resilience. The review shows that flood research has moved from traditional flood management, which provides mitigation strategies, to flood risk management, which provides an adaptation approach—adjusting mitigation measures, to flood resilience management, which provides a more resilient and sustainable plan to cope with flood disasters. We also present a detailed overview of the field of flood research, and review the definition of risk, risk analysis methods, flood management, flood risk management, flood resilience, and corresponding implementation strategies. We conclude that integrating the concept of resilience into the framework of risk management is a better approach in future flood management directions. Consequently, appropriate options and decisions prior to disaster, during disaster, and post-disaster will effectively reduce the adverse consequences using the theory of risk, resilience, and sustainability.

Risk assessment; Risk management; Flood management; Adaptation options; Resilience indicators; Management strategies.

1. Introduction

Disastrous floods driven by rapid urbanization and extreme weather events have caused millions of fatalities, and continue to cause tens of billions of dollars of direct economic loss each year. And under the background of global warming, such losses will continue to increase in the future ( Bloeschl et al., 2019 ; CRED and UNISDR, 2020 ; Hallegatte et al., 2013 ), as the intensity of extreme precipitation events increases ( Tabari, 2020 ) and the population exposed to water-related disasters rises ( Jongman et al., 2012 ; Paudel et al., 2014 ; Tellman et al., 2021 ). Worse still, river flooding, flash floods, urban floods, and coastal floods may occur simultaneously, resulting in serious compound flooding from extreme river flow, heavy rainfall, and storm surges ( Ming et al., 2022 ). Identifying the areas at risk of river flooding, urban flooding, and coastal flooding is a complicated process, as the causes of these events differ. Although it is known that flood risk increases with climate change, population growth and the increase of economic assets, and that risk is dynamic, constantly changing with underlying surface condition changes ( Hallegatte et al., 2013 ; Lai et al., 2020 ). Therefore, managing flooding to cope with increasing flood risk is urgent.

Previous research has shown the urgent need to deal with flood events ( da Silva et al., 2020 ), and it is essential to develop future flood management strategies to reduce the adverse consequences and cope with more complex types of floods. Many countries have implemented a series of practices to manage storm water, flood disasters, etc. For example, green infrastructure (GI), low-impact development (LID) and best management practices (BMPs) have been implemented in the United States; sustainable urban drainage systems (SUDS) in the United Kingdom; water-sensitive urban design (WSUD) in Australia; and low-impact urban design and development programs (LIUDD) in New Zealand ( Fletcher et al., 2014 ; Liu et al., 2017 ; Perhaps the most ambitious and far-reaching project has been the Delta Programme in the Netherlands, implemented between 2006 and 2015. This project aimed to create “room for rivers” as well as delivering some auxiliary benefits ( Rijke et al., 2012 ; Van, 2016 ). It was developed to cope with increasingly serious flood disasters, is a more sustainable method than the Netherlands’ traditional embankment measures, and has been successful in lowering the flood risk ( Asselman and Klijn, 2016 ). The European Union defined the concept of nature-based solutions (NBS) ( ECDRI, 2015 ). Pagano et al. (2019) assessed the effectiveness of NBS projects and demonstrated that NBS has positive effects on flood risk reduction and climate change adaptation. However, the lack of long-term observation records for any of these approaches has made it impossible to fully confirm the results.

China proposed the concept of the “sponge city” in 2012, which aims to adapt to environmental changes and increase a city's resilience to cope with natural disasters caused by rainfall-induced climate changes ( Guan et al., 2021 ). While constructing a sponge city is a long-term process and it will require a high initial investment for construction, previous studies have demonstrated that a sponge city can effectively mitigate urban flooding ( Hou et al., 2020 ; Li et al., 2020 ; Nguyen et al., 2019 ).

The International Conference on Flood Management (ICFM) evolved from the International Symposium on Flood Defence (ISFD), whose purpose was to discuss issues related to floods. Changing the name from “Defence” to “Management” reflected the shift of focus from flood defense and control to flood management, between 2000 and 2005; the theme of the first two conferences was flood defense, but by the third conference the theme had shifted from defense to management. From the fourth to the eighth conference the concept of flood management changed from vulnerability-based and risk-based to risk-based and resilience-based. At the ninth conference it changed further, from risk-based and resilience-based flood management to integrated resilience and sustainable flood management.

Previous review studies on flood management have mainly focused on flood risk assessment methods and flood inundation modeling, with less emphasis on detecting flood research trends ( Aerts et al., 2018 ; Lyu et al., 2018 ; Teng et al., 2017 ). This report attempts to clarify the outline and timeline of flood research and focus on the following problems:

1) Research trends and keywords for flood research;
2) The relationship between traditional flood management and flood risk management;
3) Detailed flood risk assessment methods and flood adaptation strategies;
4) The relationship between flood resilience and flood risk management.

To explore these issues, we used the literature review method to survey the changing trends of flood management strategies according to development trends over time. Based on changing trends in flood management, we provide an overview of risk assessment methods and flood mitigation, adaptation, and resilience strategies, hoping to reduce the adverse consequences of flood events and help humans cope with compound flooding under the conditions of climate change and extreme weather events. Thus, we hope that the study results will provide more adaptation measures for coping with increasing floods, for future decision-makers. Section 2 describes the research thread: from flood control to flood resilience. Section 3 presents the definitions of risk and resilience, the framework, and a detailed approach to assessing flood risk and resilience. Section 4 discusses the differences between traditional flood management, risk-based flood management, and resilience-based flood management. Section 5 provides the conclusion of this study.

2. Bibliometric analysis

2.1. keywords analysis.

To obtain the current timely and critical issues in the area of flood disaster research, we used the keywords “flood,” “floods” and “flooding” for data collection, and we chose the publication years of January 2000 to December 2020. Ultimately, 29,931 publications were found in the Web of Science (WoS) core collection database. Next, we used the literature analysis tool VOSviewer and selected all keywords, author keywords, and “keywords plus” to reveal the hot-button issues and research trends of floods referred to in previous studies ( Zhang et al., 2017 ). Author keywords were chosen by the author to best reflect the content of their research publications. “Keywords plus” means words that were generated by an automatic computer algorithm and extracted from the titles of the cited references by Thomson Reuters ( Zhang et al., 2016 ). All keywords used were obtained by combining author keywords and keywords plus. Table 1 shows the top 20 most frequently used keywords and words with similar meanings that appeared in flood research during 2000–2020. Through these frequently-occurring keywords, we found that “urban” and “basin/river-basin/catchment” were the most frequent keywords, and thus that the current research scales for studying floods are mainly cities and watersheds. From the keywords “river,” “urban,” “flash” and “sea-level rise” we found that river floods, flash floods, urban floods and coastal floods are the types of flood disasters that are currently plaguing human beings. From the keywords “climate/climate change” and “precipitation/rainfall” we suspect that floods are becoming more frequent due to climate change and extreme rainfall. The keywords “model,” “GIS,” “hydrological modelling,” “machine learning,” “HEC-RAS” and “remote sensing” are the most popular methods in the field of flood research.

Table 1

Top 20 most frequently used title words, author keywords and keywords plus used during 2000–2020.

Note: Keywords related to filters (e.g., “flood”) are not included in this table.

By perusing relevant literature and using the keywords “risk,” “risk assessment,” “risk management,” “vulnerability,” “hazard,” “management,” “adaptation,” “mitigation,” “uncertainty,” “damage/losses,” “resilience/recovery,” “forecasting” and “inundation,” we grouped current research content in flood research into the following six categories: flood risk analysis and assessment, hydrodynamic modeling and flood mapping, flood damage simulation, flood management, flood uncertainty analysis and flood forecasting. These categories are similar to those used in previous studies, which have used five categories: flood risk analysis and assessment, flood hazard mapping, flood damage assessment, flood management, and increasing the resilience of infrastructure ( Mudashiru et al., 2021 ).

2.2. Evolution of the research terms

CiteSpace is used to detect frontier research topics through burst keywords, terms and references. In this study, we applied the network analysis tool CiteSpace to identify the terms with strongest citation bursts to assess the historic evolution and research trends of flood within the 29,931 articles we selected. The term “citation burst” represents the most active areas of relevant research terms and research hotspots that have gained considerable attention. This term refers to an easily visualized output from Citespace showing the most heavily used citations (strongest “citation bursts") and the citation bursts' start and end times. The “strength” represents the strength of a citation burst. Table 2 shows the top 25 terms with the strongest citation bursts. As can be seen from Table 2 , the strongest citation burst is “flood forecasting” and the term's bursts started in 2000 and ended in 2013. The second strongest citation bursts were “flood frequency” and “climate variability,” which also had a duration of 13 years. The word pair with the highest strength is “flood frequency; ” its strength is 19.16 and the strongest citation burst occurred in the period of 2000–2012. Therefore, we can see that “flood forecasting” and “flood frequency” were the most popular and longest-lasting research topics. Detecting research terms on a time scale will reveal the trend of research topics over time.

Table 2

Top 25 terms with the strongest citation bursts.

This study through time is a change trend analysis of the research terms. Over the timeline of the literature review, the focus progressed from flood control to flood management to flood risk management to flood resilience. In the following content, this study will analyze the reasons for this evolution in research terms.

3. From flood control to flood resilience

3.1. flood control.

Flood control refers to changing the natural state of flooding through engineering measures, to reduce flood disaster. Flood control was first applied to control floods when humans realized that floods were inevitable but manageable. However, in the context of climate change, the risk of flooding is increasing, and the standards of flood control projects must change accordingly. Yet even after a series of flood control projects were implemented, flood disasters continued to occur, and human beings began to realize the limitations of flood control projects ( Kundzewicz et al., 2019 ). For example, flood control projects age over time and therefore be continuously maintained and updated, requiring investments of manpower and financial resources ( Rezende et al., 2019 ). Worse still, the cost of maintaining flood protection works may exceed the initial construction cost ( Zevenbergen et al., 2020 ). In addition, the standards for flood control projects cannot be raised without considering factors such as cost effectiveness ( Abdi-Dehkordi et al., 2021 ). Such realizations led to the introduction of the new term “flood management”: living with flooding, minimizing its losses and even deriving benefits from it where possible.

3.2. Flood management

Traditional flood management measures include structural and non-structural measures to reduce the adverse consequences of a flood event ( Sayers et al., 2013 )—detailed mitigation strategies as shown in Table 3 . Traditional flood management measures tend to protect, reduce, or eliminate impacts and actions before an event ( Peacock and Husein., 2012 ).

Table 3

Flood management measures.

Implementing structural measures is more expensive than implementing non-structural ones. Structural measures require enormous ongoing costs for maintenance, and can lead to great losses if maintenance actions are incorrect or inadequate; furthermore, ecological impacts may be higher. Non-structural measures are less expensive and more sustainable than structural ones, while being more comprehensive and having fewer negative effects ( Peacock and Husein., 2011 ). Previous studies have presented evidence that non-structural measures are easily implementable and more cost-effective than structural measures ( Dawson et al., 2011 ).

In an effort to reduce the impact of flood disasters on human life and property, humanity has gone through thousands of years of water control experience. Societies have continuously improved flood control standards through engineering measures—to keep flood waters away from humans, and non-engineering measures—to keep humans away from flood waters. However, despite all efforts, the economic losses caused by flood disasters have not been reduced, and thus finding the optimal combination of engineering and non-engineering measures has become one of the hot topics in reducing flood disaster damage. However, from the perspective of disaster reduction, human intervention only minimally affects the occurrence of natural disasters. But humans can reduce the losses from natural disasters by reducing the assets exposed in flooded areas and decreasing the vulnerability of disaster victims, as well as strengthening disaster prevention and mitigation capabilities. Thus, flood management strategies based on both structural and non-structural measures have been transformed to risk-based flood management strategies.

3.3. Flood risk management

Flood risk management includes risk analysis, risk assessment and risk reduction. Risk analysis refers to the determination of the risks; risk assessment refers to the classification of the risks; and risk reduction refers to providing flood risk management strategies ( Samuels et al., 2009 ). Flood risk assessment and management before a disaster can effectively reduce disaster losses ( Dhiman et al., 2019 ; Lai et al., 2020 ; Pham et al., 2021 ). An accurate understanding of flood risk and its drivers is crucial for effective risk management ( Muis et al., 2015 ). Therefore, it is essential to perform flood risk assessment and adopt appropriate flood management measures, engaging both ordinary citizens and flood managers, before a disaster occurs.

3.3.1. Flood risk

The concept of risk is not universally defined; different disciplines have different definitions of risk. But generally, risk is defined as (i) the uncertainty of future results; (ii) the uncertainty of the occurrence of losses; and (iii) the combination of the probability of future events and their possible consequences ( Jonkman et al., 2003 ; Apel et al., 2008 ; Wang et al., 2019 ). For example, the IPCC AR3 report describes risk as a function of probability and consequence ( IPCC, 2001 ); in IPCC AR5 risk is often represented as the probability of the occurrence of hazardous events or trends multiplied by the impacts if these events or trends occur ( IPCC, 2014 ), as shown in Table 4 . In order to maximize the consistency of the use of IPCC groups, IPCC AR6 redefined risk as the potential adverse consequences for human or ecological systems, recognizing the diversity of values and objectives associated with such systems ( IPCC, 2019 ).

Table 4

Risk as defined in the IPCC reports.

In other words, there has been a transition from IPCC AR4 to IPCC AR5, from vulnerability-based to risk-based climate change adaptation concepts. Since this change, risk assessment research has received widespread attention. IPCC AR4 defined vulnerability as comprising three factors: exposure, sensitivity, and adaptive capacity ( IPCC, 2007 ), the model as shown in Eq. (1) . However, in IPCC AR5, vulnerability includes two elements: sensitivity, and capacity to cope and adapt ( IPCC, 2012 ), the model as shown in Eq. (2) .

The IPCC SREX report defined the risk determined by climate and weather events (the hazards), exposure, and vulnerability ( IPCC, 2012 ). IPCC AR5 defined the risk framework interactions among vulnerability, exposure, and hazard ( IPCC, 2014 ), the model as shown in Eq. (3) . The flood risk defined in recent studies has been based on the risk framework defined in IPCC AR5; it is determined by vulnerability, exposure, and hazard. Vulnerability includes the concepts of sensitivity and adaptive capacity, the model as shown in Eq. (4) .

3.3.2. Flood risk assessment

The risk assessment of natural disasters includes qualitative, semi-quantitative, and quantitative approaches. The result of qualitative assessment is the relative magnitude of natural disaster risk, such as zero risk, low risk, medium risk, and higher risk ( Ming et al., 2022 ). The result of semi-quantitative risk evaluation can be expressed as the multiplication of the frequency grade and consequence grade ( Bai et al., 2013 ). Quantitative assessment converts the loss result into a monetary value, to obtain an expected loss, such as the expected annual loss (EDA) or the cumulative loss. In order to accurately measure the impact of flood disasters on human societies and economies, flood risk assessment has undergone a change from qualitative to quantitative. According to different research needs, flood risk assessment could choose the research scale (i.e., global, country, basis, city, community) ( de Moel et al., 2015 ). When conducting flood risk assessment, it can be assessed according to different years to observe the characteristics of changes in flood risk over time. In addition, it can be assessed according to specific scenarios, such as different flood return period scenarios, different social development scenarios, and different flood adaptation scenarios ( Cheng et al., 2013 ; Penning-Rowsell et al., 2013 ; Shan et al., 2019 ).

The most frequently used expressions of risk assessment models are the expressions of “addition” and “multiplication”. The expression based on “plus” is a linear risk evaluation model; the equation for calculating the flood risk is defined in Eq. (5) . The expression based on “multiplication” is an index model; the equation for calculating the flood risk is defined in Eq. (6) .

Flood risk assessment from hazard, exposure, and vulnerability deals with the relationship between floods and humans. This approach can identify more effective counter-measures from these three components, for disaster risk reduction. Koks et al. (2015) deem flood risk assessment to be estimates of the loss of life and economic damage. Traditional methods include a probability evaluation method based on historical data, comprehensive flood risk assessment, flood risk assessment integrating remote sensing and a geographic information system (GIS), and the Source-Pathway Receptor conceptual model. Nowadays, in the era of big data and the synthesis of flood risk assessment approaches, the risk assessment approach is being increasingly oriented toward scenario-based methods ( Zhang et al., 2020 ). The following section describes flood risk assessment in detail. A synthesis of flood risk assessment approaches includes the three indicators of hazard, exposure, and vulnerability. A scenario-based flood risk assessment requires (1) a hydrodynamic model and (2) flood damage simulation.

3.3.2.1. Synthesis of flood risk assessment approaches

Most risk assessments belong to the category of comprehensive assessment. Comprehensive assessment means to make a general assessment of the index data extracted from different aspects of objective entities. The first step is to build an evaluation index system, such as selecting an index system from the three indicators: hazard, exposure, and vulnerability. Some studies have taken the perspective of systems theory, such as disaster-pregnant environment, disaster-causing factors, disaster-bearing bodies and defense capabilities; the indicator system is selected in terms of these aspects. The second step is to select each index factor and collect its related data ( Asbridge et al., 2021 ; Roy et al., 2021 ); Figure 1 shown the detailed indicators of the three elements of risk. When selecting indicators, the indicator systems should be considered according to the principles of purposeful, systematic, scientific and actionable. When selecting data, the actual situation of the study area and the difficulty of data acquisition should be considered. Third, the weight of each index and factor is determined by certain mathematical methods, such as AHP, the entropy weight method, the fuzzy comprehensive evaluation method, etc. Finally, hazard, exposure, vulnerability and adaptability are calculated by either a linear or an exponential evaluation model, to obtain the results of flood disaster risk analysis ( Jiang et al., 2008 ).

Summary of the indicators of the three elements of risk.

3.3.2.2. The simulation-based flood risk assessment approach

Analyzing the risks of flood disasters in advance is an effective approach to alleviating the losses induced by floods. The simulation-based approach merges multidisciplinary approaches, which can not only calculate flood risk but also simulate the flood evolution and evaluate damage losses. Therefore, more and more studies are applying this method to carry out flood risk and flood losses research ( Li et al., 2016 ).

According to the framework of risk assessment, simulation-based flood risk assessment can be divided into two parts. The first part is a hydrological and hydrodynamic model based on a hazard analysis to obtain a flood inundation map. The second part is a damage estimation model based on vulnerability and exposure analysis to obtain disaster loss results. The simulation process is shown in Figure 2 .

(1) Hydrological and hydraulic models based on hazard analysis

Framework of simulation-based flood risk assessment approach.

A hydrological model is used to simulate the runoff and confluence process of a watershed. It can simulate the runoff process of rainfall from the source, to obtain the flow processes and flow peak values of different sections of the rivers, but it cannot determine the hydraulic elements of the river or the flood inundation range of the watershed. The hydrodynamic model can simulate the evolution of floods and can directly reflect the inundation range and depth of floods in the form of a watershed inundation map, but it cannot simulate the hydrological process from sources such as precipitation, evaporation, or runoff. After coupling the hydrological model with the hydrodynamic model, the flow process of the channel section simulated by the hydrological model can be used as the input to the upstream boundary conditions of the hydrodynamic model, which reflects the runoff change in the basin and the evolutionary process of the flow in the river.

(2) Damage estimation model based on vulnerability and exposure analysis

The damage estimation model is usually adopted to estimate the damage costs of flood disasters. Questionnaire survey and stage-damage functions are two basic methods for conducting flood damage estimations ( Win et al., 2018 ). A questionnaire survey is a reliable method, but is expensive in terms of both funding and time. The stage-damage function method is therefore more widely used than the questionnaire survey, for estimating flood damage.

The stage-damage functions method comprises the following four procedures:

a) Identify exposed elements and collect relevant socioeconomic data;
b) Calculate the exposed asset value in each unit;
c) Build the stage-damage curve according to flood water's inundation depth and the receptor loss rate;
d) Calculate direct monetary damage according the stage-damage curve and asset value in each unit.

A stage-damage curve describes the change of the damage fraction of different receptor types, with the change in the flood inundation depth. However, the relation between inundation depth and damage fraction is uncertain, as it can vary among different regions. It is difficult to measure if the stage-damage curve is used in multiple regions, as this will add extra uncertainty to the modeling process ( Scorzini and Frank, 2017 ). On the other hand, the scenario-based flood risk assessment approach can simulate the dynamic process of flood occurrence and quantitative disaster loss results, but because of its high requirements for basic data, it poses great operational difficulty.

3.3.3. Flood risk management strategies

Flood risk management means minimizing the loss of life and economic damage by flood disasters or reducing the probability, and the adverse consequences, of flooding. Flood risk management includes not only implementing structural measures to reduce the possibility of flooding (reducing hazard) but also using non-structural measures to reduce the amount of assets exposed and the vulnerability of receptors. Sayers et al. (2013) reported that the purpose of flood risk management strategies was to achieve four goals: (i) reduce risk to people and communities, (ii) reduce risk to and promote economies, (iii) promote ecosystem goods and services, and (iv) promote social well-being.

Some literature have used the three elements of flood risk to provide flood risk management (FRM) strategies, such as reducing the exposure of humans, the economy, and the ecosystem to flooded areas and reducing the vulnerability of those exposed to floods ( Koks et al., 2015 ; Sayers et al., 2013 ). Other literatures have used a flood risk management (FRM) framework to provide flood risk management strategies (FRMSs), which include flood defense, flood prevention, flood mitigation, flood preparation and flood recovery ( Dieperink et al., 2016 ; Hegger et al., 2014 ; Raadgever and Hegger, 2018 ). At the same time some studies have put forward flood risk management strategies based on the Source-Pathway Receptor conceptual model.

3.4. Flood resilience management

3.4.1. flood resilience.

Flood risk is increasing with climate change and socio-economic development. Therefore, current flood risk management measurements are not sufficient to cope with today's flood risk ( Rezende et al., 2019 ; Ward et al., 2017 ). The concept of resilience has been widely used in recent academic literature and policy documents. IPCC AR6 defines resilience as the ability to bounce back after a disturbance and returning to the previous state, and adaptation is often organized around resilience ( IPCC, 2022 ). Table 5 shows the definitions of resilience in IPCC, UNISDR and other reports.

Table 5

Resilience defined in IPCC and UNISDR reports.

3.4.2. Flood resilience assessment

Flood resilience focuses on building flood resilience indicators and evaluating flood resilience, in the primary literature. The major flood resilience evaluation are based on the semi-qualitative approach, to select the indicator systems or interviews from various dimensions and express the importance of flood resilience. Table 6 presents the dimensions of resilience in previous studies, and the research scale. Sun et al. (2016) , based on the methods of analytic network process evaluation flood disaster resilience in the Chaohu Lake Basin, developed an index system for evaluating regional flood disaster resilience, and a flood resilience index system that included five dimensions (nature, society, economy, technology, and management). Luo et al. (2021) evaluated flood disaster resilience in the Yangtze River Basin based on the hesitant fuzzy linguistic term and pointed out that the flood resilience index system includes five dimensions—nature, society, economy, infrastructure, and management. Other researchers have evaluated flood resilience based on resilience theory (robustness, rapidity, redundancy, and resourcefulness) ( Lee et al., 2021 ). Resilience can also be measured by the time a receptor needs, to recover from shock ( Park et al., 2021 ).

Table 6

The dimensions of resilience in previous studies.

3.4.3. Flood resilience management strategies

Many projects have been put forward, that focus on building resilient communities, resilient cities, and resilient coastal areas. The Resilient Communities Project, the 100 Resilient Cities program, and the Sustainable and Resilient Coastal Cities program are examples that have been implemented in various countries. Some institutions and international teams, such as the OECD, are also building resilient cities; the Rockefeller Foundation pioneered a project to build 100 resilient cities; and a smart, sustainable and resilient cities project has been implemented by the G20.

Most flood resilience management strategies use a resilience framework to provide management strategies ( Abdi-Dehkordi et al., 2021 ; Kim et al., 2017 ; Rezende et al., 2019 ). The community resilience framework is based on four main components (economic activities, ecosystem services, infrastructure and buildings, and community action) and their sub-categories, to provide detailed resiliency solutions ( https://resilientvirginia.org/ ). The framework also defines the characteristics of resilience (aware, diverse, self-regulating, integrated, and adaptive) as detailed indicators, and provides resilience strategies for each indicator ( Rodin, 2013 ). The resiliency solutions could quickly bring infrastructure such as buildings back to its initial (pre-disaster) state, and the community/city could also recover quickly, perhaps even attaining a state better than its pre-disaster one ( Cariolet et al., 2019 ; Koren et al., 2017 ).

We need to explain the difference between infrastructure resilience and community/city resilience. A disruptive event can be divided into three stages: before disruption, during disruption, and after disruption. When disruptive events occur, infrastructure has four resilience properties, defined as the 4R's: Robustness, Redundancy, Resourcefulness, and Rapidity, and a community/city has five resilience properties: Robustness, Redundancy, Rapidity, Resourcefulness, and Adaptivity. The similarity between these two types of resilience characteristics—engineering and community/city—is that the system can experience a disturbance and still retain control of its function and structure.

Some infrastructure, such as buildings and roads, etc., is in the domain of engineering resilience. When disruptive events occur, engineering resilience will enable the infrastructure to recover to its initial state within a defined timeline, as shown in Table 7 . Communities and cities, by contrast, belong to the category of socio-ecological resilience; after a disturbance, they will reach a new equilibrium within a defined timeline. Because the operation of a city involves human interaction with natural systems, such a system can learn from past disasters and improve its ability to adapt to disasters, establishing a new equilibrium that will be better than the initial state of the resilient city system.

Table 7

Resilience properties and the schematic of resilient system.

4. Discussion

4.1. trends in historical research.

We applied the software of CiteSpace to identify the research trends and timelines from 29,931 articles. Figure 3 summarizes the timelines, measures, and research purposes from flood control research to flood management research from 2000 to 2020. The research about flood management strategies was divided into four distinct phases of its development, which are shown in Figure 3 .

a). Phase-I: From 2000 to 2003, the concept of flood management is using structural measures to control floods. Protecting Life and Property from flooding was the advantage of defense measures; however, it is expensive to install and maintain, and it will reduce the biodiversity around embankments and dams ( Liao et al., 2019 ; Sharafati et al., 2020 ).
b). Phase-II: From 2003 to 2008, the concept of traditional flood management aimed to reduce the impact of flood events; however, it failed to deal with over-standard floods and difficulty addressing the uncertainty of flood events ( Anita, 2013 ). Flood mitigation measures aim to reduce the losses from flood disaster while including structural and nonstructural measures to cope with flooding.
c). Phase III: From 2008 to 2017, flood risk management is cost-effective and has environmental benefits, while there are always residual risks, and it is difficult to eliminate residual risk ( Bischiniotis et al., 2020 ; Merz et al., 2010 ). Flood adaptation measures are aimed at reducing vulnerability, effects and undertaking actions to strengthen and adjust the existing mitigation measures against the adverse effects of floods; these measures include soft adaptation and hard adaptation ( Du et al., 2020 ; Logan et al., 2018 ).
d). Phase IV: 2017 to 2020 onwards. Flood resilience measures aim to reduce and transform the risk of flood damage and quickly recover the system to its pre-flood state after flooding; it focuses on absorptive coping capacity, adaptive capacity, transformation capacity and anticipatory capacity ( Mahzarnia et al., 2020 ; Saja et al., 2019 ).

Research trends of flood risk management.

We can see that flood management measures are continually being optimized, and research purposes are becoming more diverse. After the concept of sustainable development and the promotion of some international conferences, people gradually realized the limitations and disadvantages of the previous management measures, so the flood management measures have gradually changed to more sustainable strategies. Flood defenses measure fail to deal with over-standard floods. Traditional flood measures cannot address changing flood risks. There is always residual risk in flood risk management. Therefore, the resilience measure gradually becomes a new trend in flood management.

4.2. Flood risk management and flood resilience

Recently, there has been more research on flood resilience than on flood risk management. Previous studies put forward three relationships between resilience and risk management: resilience as the goal of risk management, resilience as part of risk management, and resilience as an alternative to risk management ( Suter, 2011 ). Nevertheless, this study has taken the approach of resilience as part of risk management, because resilience as an alternative to risk management is too radical to consider seriously at this time. However, risk is inherently unpredictable, and it is impossible to prevent risks completely. Thus, some residual risk always exists.

Flood risk management requires human recognition; however, human recognition can never be complete or absolute. Therefore, flood risk management has the limitation of not being able to eliminate all risks. If resilience could deal with the remaining risks and complement the inherently insufficient risk management, it would be beneficial to increase resilience. Further research, therefore, will focus on how to build resilience and evaluate resilience to complement the inherently insufficient risk management. Comparing risk-based flood management strategies with resilience-based flood management strategies, as shown in Figure 4 , we find the advantage is that the resilience-based flood management strategies enables the receptor to recover during an event. Thus resilience-based flood management strategies may enable a system to recover to a state that is better than its pre-disaster state, because of the capabilities of self-organization, learning, and adaptation. Because flood resilience can cope with unexpected climatic perturbations and is self-organizing ( McClymont et al., 2019 ), flood risk management has shifted toward more resilience, such as NBS, blue-green infrastructure, LID, GI, or SCP.

Risk-based and resilience-based flood management strategies.

4.3. Future research trends

IPCC AR5 shows that extreme precipitation events will become more intense and frequent in many parts of the world ( IPCC, 2014 ). A combination of climate change mitigation and adaptation measures applied simultaneously is most conducive to mitigating the negative impacts of climate change on humans ( Yamamoto et al., 2021 ). Previous researches have shown that adapting to climate change is a good way to address a range of problems caused by rising temperatures and sea levels ( Xu et al., 2019 ). Mitigation measures aim to reduce global greenhouse gas emissions to mitigate the effects of global warming. Zhou et al. (2018) found, however, that climate change adaptation was more effective in reducing future flood volumes than was climate change mitigation. For example, representative concentration pathways (RCPs) and shared socioeconomic pathways (SSPs) aim to stabilize the concentration of greenhouse gases in the atmosphere. However, mitigation measures have limitations. Even if appropriate mitigation measures are developed and implemented, temperature increases will continue for centuries ( IPCC, 2007 ; IPCC, 2014 ).

With the release of IPCC AR6, the concept of climate change resilience will become the hot-spot and research frontier in the future. Climate resilience is a new direction for coping with climate change, although climate change mitigation and adaptation will continue to be important. Human and natural systems can build resilience through adaptation, mitigation, and sustainable development ( IPCC, 2021 ). Because resilience has the advantages of transformation, self-organization, and learning capacity, flood management will trend toward resilient management strategies in the future. Therefore, implementing flood resilience strategies, evaluating flood resilience, detecting new problems in resilience theory, and improving resilience through intervention measures will be hot issues in future flood research; indeed, they are already becoming more popular research approaches.

Flood risk is a dynamic process that changes with drivers, such as climate change, urbanization, sea-level rise, land subsidence, and socioeconomic development. Therefore, how to improve the Spatio-temporal resolution of flood risk assessment to provide more detailed flood risk information for disaster risk reduction will become a future research direction. Compound disasters and multi-hazard events will further aggravate the impact of disaster events, such as Australia's drought-wildfire-heavy Rain Event in 2019/2020 and Floods events under the COVID-19 pandemic ( Kemter et al., 2021 ; Simonovic et al., 2021 ). In addition, with the development of the Internet, the occurrence-development-impact of a flood disaster event will be quickly discussed by the public, so the management of disaster events will bring huge challenges to managers.

Therefore, how to play the function of resilience in robustness and redundancy to ensure the normal operation of infrastructure during the process of extreme disasters will become one of the important tasks in the future. Secondly, resilience management should fully exert the people's subjective initiative and adjust adaptive means according to the current flood risk at any time. To maximize benefits, how to fully integrate the four dimensions of resilience management (i.e., Plan, Absorb, Recover, Adapt) and risk management (i.e., Mitigate, Prepare, Respond, Recover) will become a future research focus. In addition, the R&D and application of new technologies will also become an important tool for disaster resistance and rescue in the future, thus continuously replacing the traditional management methods in the past. For example, combined mobile flood walls are used to protect the urban areas and resist floods. The drones provide precise positioning services for disaster relief; the waterlogging truck is used to quickly drain the waterlogged water. The emergency-powered boat bridge is used to transfer the trapped people.

5. Conclusion

This study used bibliometric tools and selected 29,931 academic literature to explore the changing trends of research topics in the flood management field over time. We have also presented detailed content on the definition of risk, risk analysis methods, flood management, flood risk management, flood resilience, and corresponding implementation strategies. Flood management is transitioning from risk-based to resilience-based. Hence, we explored the links between flood defense, flood management measures, flood risk management strategies, and flood resilience management strategies. This study shows that flood control strategies have been unable to respond to today's flood environment. Flood risk is unavoidable but manageable; it can be minimized or diverted through engineering and non-engineering measures. Flood risk management embeds the concept of a continuous adaptation process, replacing the approach of implementing and maintaining flood control measures. Flood resilience embeds the concept of sustainability, integrated with the concept of a continuous process of adaptation, in flood risk management. Flood management strategies will be re-integrated with sustainability, resilience and adaptation, in the future.

By comparing flood mitigation, adaptation, and resilience measures, we find that mitigation measures aim to resist flooding and take action, and adaptation measures have accepted the inevitability of flood events and adjusted the mitigation measures, thus becoming more suited to the actual environment. Because resilience measures focus on learning from the experiences of flood events, this approach can lead to better adaptation measures. Flood risk management integrates flood mitigation and adaptation to cope with flood events, and flood resilience can therefore reduce and transform damage risk. This study prefers the view of integrating the concept of resilience into the framework of risk management. However, although flood resilience management strategies have advantages over flood risk management strategies, it is unreasonable to attempt to replace risk management completely.

This report provides new insight into flood research trends, by examining current research frontiers, and clearly shows a timeline for flood research. It will help stakeholders understand the advantages of the different strategies of traditional flood management, flood risk management, and flood resilience. The next step for stakeholders is facing uncertain climates, diverting human-induced disasters, and building more resilient communities, cities, and watersheds. This study suggests flood adaptation and mitigation measures along with the integration of the dual strategies of flood risk management and flood resilience, to effectively reduce water-related adversities.

Declaration

Author contribution statement.

All authors listed have significantly contributed to the development and the writing of this article.

Funding statement

Dr. Shenghui Cui was supported by National Natural Science Foundation of China [41661144032].

This work was supported by CAS Key Laboratory of Receptor Research [132C35KYSB20200007], National Natural Science Foundation of China [71961137002].

Data availability statement

Declaration of interest's statement.

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

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The Perception of Flood Risks: A Case Study of Babessi in Rural Cameroon

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Published: 18 May 2021
Volume 12 , pages 1–21, ( 2021 )

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Gertrud Buchenrieder 1 , 2 ,
Julian Brandl 3 &
Azibo Roland Balgah 4

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Although risk perception of natural hazards has been identified as an important determinant for sound policy design, there is limited empirical research on it in developing countries. This article narrows the empirical literature gap. It draws from Babessi, a rural town in the Northwest Region of Cameroon. Babessi was hit by a severe flash flood in 2012. The cross-disciplinary lens applied here deciphers the complexity arising from flood hazards, often embedded in contexts characterized by poverty, a state that is constrained in disaster relief, and market-based solutions being absent. Primary data were collected via snowball sampling. Multinomial logistic regression analysis suggests that individuals with leadership functions, for example, heads of households, perceive flood risk higher, probably due to their role as household providers. We found that risk perception is linked to location, which in turn is associated with religious affiliation. Christians perceive floods riskier than Muslims because the former traditionally reside at the foot of hills and the latter uphill; rendering Muslims less exposed and eventually less affected by floods. Finally, public disaster relief appears to have built up trust and subsequently reduced risk perception, even if some victims remained skeptical of state disaster relief. This indicates strong potential benefits of public transfers for flood risk management in developing countries.

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Johnny D. Dariagan, Ramil B. Atando & Jay Lord B. Asis

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Stéphane Hallegatte, Adrien Vogt-Schilb, … Chloé Beaudet

Avoid common mistakes on your manuscript.

1 Introduction

Escalating natural hazards often cause enormous property damage, thereby increasing victims’ vulnerability Footnote 1 . In the last few decades, the number and volatility of extreme natural hazards increased tremendously (CRED 2019 ; Statista 2020 ). According to the Center for Research on the Epidemiology of Disasters (CRED), the frequency of natural hazard-related disasters is on the rise, as is the damage they cause (CRED 2019 ). Hydrometeorological hazards alone accounted for 87% of all hazards, with floods representing about 50% of all weather related hazards between 1995 and 2014 (Birkholz et al. 2014 ; World Bank 2014 ).

Economic losses due to natural hazards and disasters are concentrated in high-income countries. Footnote 2 Nevertheless, there is a general consensus that natural hazards are more severe in developing countries, especially in terms of their negative and largely irreversible impacts on livelihoods, natural and physical resources, and ecosystems (Ahsan 2011 ; Fondo et al. 2018 ). Around 85% of the globally affected population lives in developing countries and 47% reside in rural areas (UN DESA 2015 ). In fact, over 90% of the population in developing countries lives in rural areas (UN DESA 2015 ). According to Lumbroso et al. ( 2016 ), floods and droughts are the two natural hazards that have had the largest humanitarian impacts in Africa over the past 30 years based on the number of people affected. However, in the past decade, across Africa, floods have overtaken droughts in terms of the number of people that they impacted negatively (Lumbroso 2020 ). Kendon et al. ( 2019 ) forecast many extreme outbreaks of intense rainfall over the next 80 years across Africa, triggering devastating floods with loss of property and life, displacement, and disruptions of farming. They emphasize that intense rainfalls that were forecasted to occur in a region every 30 years are more likely to happen every three to four years, mainly due to climate change.

Flood risk perception affects risk management and, therefore, determines whether risk management is successful in reducing community vulnerability or not (Bubeck et al. 2012 ). Although risk perception of natural hazards has been an important topic for a few decades now, there is still limited empirical research on flood risk perception in sub-Saharan African countries and even less in rural regions (Fondo et al. 2018 ). We contribute to closing this research gap by examining risk perception among victims of a flash flood in the rural town of Babessi in the Northwest Region of Cameroon, which occurred on 9 September 2012. Footnote 3 The article attempts to answer the question: What determines the flood risk perception of the residents of a rural town in Cameroon who have been disastrously hit several times by a flash flood? Justification for place-based risk research arises from the fact that (1) no two natural (flood) hazards are exactly the same; (2) flood effects are influenced by regional and social context; and (3) flood hazard management strategies should be context adapted (Hisali et al. 2011 ; Fondo et al. 2018 ; Lechowska 2018 ). We approach the research with a cross-disciplinary theoretical grounding. This approach recognizes the complexity inherent to risk perception analyses.

Risk perception is considered as an individual and intuitive assessment of the perceived risk of a hazard and its often disastrous consequences in the context of limited or uncertain information (Slovic 2000 ). Risk perception influences eventual response to the hazards (Peters and Slovic 1996 ). Risk perception analysis thus reveals what people perceive (awareness), why they perceive it that way (dread Footnote 4 ), and how (if at all) they will subsequently manage the negative effects linked to the hazard (preparedness) (Lave and Lave 1991 ; Wachinger et al. 2013 ). Lechowska ( 2018 ) summarizes awareness, dread, and preparedness as the “triangle” of risk perception. From a policy point of view, risk perception and its influencing factors determine the attitude (the level of preparedness for a flood) and the possible behavior of potential victims when actually faced with a flood (Gebrehiwot and van der Veen 2015 ). According to Rogers and Prentice-Dunn ( 1997 ), risk perception and the associated threat of pain and suffering motivate people to take protective action. In addition, knowledge of the factors affecting risk perception is primordial for more effective flood information and management strategies (Birkholz et al. 2014 ; Lechowska 2018 ).

The next section reviews the literature on risk perception related to natural hazards and summarizes the cross-disciplinary theoretical grounding for risk perception research. Importance is attached to clearly differentiate between relevant terms in risk perception research, namely natural hazard, disaster in a social context, risk, and risk perception. The comprehensive theoretical grounding is pertinent for identifying and soundly interpreting factors affecting risk perception. Section 3 highlights the study area and discusses the materials and methods. The place-based risk research is linked to the rural town of Babessi in Cameroon. This town has an increased risk of flash flooding Footnote 5 because it is situated in the Ngoketunjia Division on the Ndop Plain, a valley surrounded by a mountain chain consisting of Bamenda Mountain and the Oku Massif. Section 4 , organized along the major paradigms theorizing risk perception, presents and discusses the empirical results. The final section concludes and summarizes limitations of the research.

2 Natural Hazards, Disaster, Risk, and Theoretical Grounding of Risk Perception

Natural hazards are natural, but hazards do not have a disastrous impact without a social context. Risk is a probabilistic measure of hazards, whereby meanwhile socioeconomic factors are included in the objective risk measure. Therefore, the nature of hazards is classified as well as their social impact in the form of disasters. Risk perception in the context of the occurrence of a hazard captures the subjective evaluation of the hazard and the resulting possible disaster. After having clarified this hazard-disaster-risk perception continuum, the most prominent theoretical paradigms underlying risk perception are summarized.

2.1 From Natural Hazard to Disaster

In general, hazards are “threats to humans and what they value” (Kates and Kasperson 1983 , p. 7029; Slovic et al. 1985 , p. 91). In other words, hazards are the threatening events (Bradford et al. 2012 ). Based on their nature, hazards can be further divided into (1) natural hazards, (2) technological hazards, and (3) violence and war hazards. This contribution focuses on natural hazards. There are numerous, more general definitions of natural hazards. In spite of disparities, they are generally conceived as uncommon and extreme events of geophysical, atmospheric, hydrological, or biological nature with the potential to cause harm (for example, loss of life) or loss (for example, loss of property, economic disruption, and environmental damage) (Benson and Clay 2004 ; Alexander 2009 ; UNISDR 2009 ; Bokwa 2013 ).

Natural hazards can be further classified on the basis of their origin: (1) within earth such as earthquakes, (2) on earth’s surface such as landslides and floods, and (3) above the earth such as storms (Stillwell 1992 ). Another classification follows the time of occurrence, the speed of onset, and the duration of the natural hazard (Bokwa 2013 ). Some hazards can occur at any time of the year (for example, tsunamis or thunderstorms) whereas others occur at a certain time of the year (for example, hurricanes). Hurricanes for instance can be detected hours or days in advance. However, earthquakes cannot be precisely forecasted. With regard to the duration and impact, an earthquake may last for seconds (but the aftershocks go on for weeks), and a flood a few minutes or for weeks; both with effects that can be of long duration (Middelmann 2007 ). Droughts differ from other natural hazards due to their slow-onset and the difficult determination of their end (Wilhite 2000 ). Naturally, a drought simply means lack of rainfall (Neisi et al. 2020 ).

A natural hazard can lead to a disaster (Bokwa 2013 ). However, there exists no generally agreed definition of what constitutes a disaster (Alexander 2000 ; Smith 2013 ). According to Sivakumar ( 2005 ), a natural hazard-related disaster exists when a hazard leads to socioeconomic and/or environmental calamitous consequences. The Munich Reinsurance Company speaks of a disaster “if any property is damaged and/or any person [is] sincerely affected (injured, dead)” (Below et al. 2009 , p. 3). The EM-DAT under CRED defines a disaster as involving 10 or more people being killed and/or 100 or more people being affected and/or if the affected country/region declares a state of emergency or is calling for international assistance (CRED 2019 ). Similarly, UNISDR ( 2009 , p. 9) outlines a disaster as a “serious disruption of the functioning of a community or a society involving widespread human, material, economic or environmental losses and impacts, which exceeds the ability of the affected community or society to cope using its own resources.” Therefore, and irrespective of consistency in definitions, a disaster seems to only exist if the hazard results in some serious losses, however defined.

2.2 Hazard versus Risk

What is risk? Are risks synonymous to hazards? Kates and Kasperson ( 1983 , p. 7029) distinguish hazard and risk as follows: “A hazard [...] is a threat to people and what they value (e.g., property, environment, future generations, etc.) and a risk is a measure of hazards.” Risk can thus be defined as the “probability of some adverse effect of a hazard” (Short 1984 , p. 711). Kron ( 2002 , 2005 ) defines a flood risk as follows: Flood risk $=$ hazard × values × vulnerability.

This more sophisticated definition portrays risk as the intersection between a hazard, the exposure of people/assets to the hazard, and the vulnerability of the people/assets that are exposed (Birkholz et al. 2014 ). This definitional approach is inherent to socioeconomic research in the field of natural hazards. Sociological and behavioral research considers risk as an inherent attribute of human decision making (Bonß 1996 ; Birkmann 2013 ). Actions or events (such as hazards) prompt decisions whose variability leads to different consequences (Luhmann 1993 ). Consequently, the decision-making process connotes taking a risk, for instance when deciding to get or not to get prepared for a natural hazard, possibly occurring in the future (Bonß 1996 ; Rogers and Prentice-Dunn 1997 ; Birkmann 2013 ; Birkholz et al. 2014 ). In such a social system, the willingness and capability to take a risk is also influenced by the interaction between the potential disaster resulting from a hazard (Bokwa 2013 ) and the individual, physical, and social vulnerability of possible victims (Kron 2002 , 2005 ). Thus, disaster victims not only face different probabilities of experiencing adverse effects of a hazard but also display different degrees of vulnerability. The concept of vulnerability has become particularly important within natural hazard and disaster research because it encapsulates how social contexts shape risk (Birkholz et al. 2014 ). The social context of vulnerability interacts with the geographic context to create “place vulnerability” (Rogers and Prentice-Dunn 1997 ; Birkholz et al. 2014 ). This may lead to varying decisions and thus to varying risk perceptions (and vice versa). Definitions of “risk perception” must inevitably draw from characterizations of risk itself (Birkholz et al. 2014 ). Risk perception has achieved widespread recognition in the general risk management literature because “perceptions of risk and risk related behaviors may amplify the social, political, and economic impact of disasters well beyond their direct consequences” (Burns and Slovic 2012 , p. 579). Furthermore, most people include experiences, emotions, and feelings in their perception of risk, producing a measure that relies on intuitive risk judgements (Bradford et al. 2012 ).

2.3 Cross-Disciplinary Theoretical Grounding of Risk Perception Research

Discourses on risk perception are fundamentally rooted in the social sciences and psychology. Traditionally, risk perception research investigates how individuals evaluate (and react to) risks associated to a hazard (Luhmann 1993 ), whereby a hazard can lead to a disaster when facilitated by social context characteristics. Risk perception therefore captures the subjective evaluation of the probability of the occurrence of a certain calamity and the individual’s anxiousness with the consequences (Slovic 1987 , 2000 ; Sjöberg et al. 2004 ; Bradford et al. 2012 ; Lechowska 2018 ). This subjective assessment of risk depends on the type and magnitude of risk faced, the individual’s vulnerability, the social context, and psychological or cognitive attributes such as former experience with the hazard in question (Oltedal et al. 2004 ; Wachinger et al. 2013 ), the individual’s perceived capacity to take action, and the protective response efficacy (Rogers and Prentice-Dunn 1997 ; Groothmann and Reusswig 2006 ; Mertens et al. 2018 ). Underlying risk perception is the process of collecting, selecting, and interpreting signals about uncertain negative impacts of hazards (Wachinger and Renn 2010 ).

The theoretical paradigms of risk (perception) research can be broadly delimited along psychological, normative, cultural, cognitive, and social constructions. In the following, prominent theoretical paradigms of risk perception research are summarized. We follow here the call of Birkholz et al. ( 2014 ) to re-examine and re-invigorate flood risk perception research by more constructivist thinking. This review structures the cross-disciplinary lens to risk perception research adopted here, in order to uncover parameters that often lie latent in disciplinary narratives. They include: (1) mental models; (2) psychometric paradigm; (3) orienting dispositions such as affects and worldviews; (4) socioeconomic and demographic models; (5) cultural theory; (6) trust-oriented concepts; (7) location and experience-oriented concepts; and (8) the protection motivation theory.

2.3.1 Mental Models and Risk Perception

Mental risk perception models capture how people think about their particular hazard situation, what they know, and what they (mis)perceive about the facts and processes related to causation, damage, and mitigation of eventual hazards (Peters and Slovic 1996 ). The mental model approach reveals surprising insights. An interesting finding is that people appear to deny the possible reoccurrence of a hazard they experienced, saying it could not or would not happen again. Also, when asked about what they know about floods, they focused on the largest flood they had experienced rather than on the most recent flood (Lave and Lave 1991 ). Under such circumstances, those affected are often reluctant to take precautionary measures. Instead of concluding that people do not heed advice on mitigating hazardous effects, the true reason may be ignorance, fatalism, or a perceived lack of self-efficacy and measure-efficacy (Rogers and Prentice-Dunn 1997 ; Mertens et al. 2018 ). The mental model approach is often supplemented by demographic information. Education for instance may influence how a hazard is perceived because it affects the individual’s cognitive maps. Furthermore, education influences how and which information is sought and how knowledge is generated. Direct hazard experience may also influence these cognitive maps (Lechowaska 2018 ). Mental models are quite robust and have found solace in the psychometric paradigm as well as in the concept on affect and worldviews (Peters and Slovic 1996 ), further explained in Sect. 2.3.2 and 2.3.3 .

2.3.2 Psychometric Paradigm and Risk Perception

The psychometric paradigm, mainly driven by Fischhoff et al. ( 1978 ) and Slovic ( 1987 ), depicts that risk is subjectively perceived, whereby each individual is influenced by a wide array of psychological, social, institutional, and cultural factors (Sjöberg et al. 2004 ). Risk perception research that is based on the psychometric paradigm identified two factors, namely dread and novelty of the risk, explaining a substantial part of the variation in risk perception. A dread risk is defined by the extent of perceived lack of control, feeling of dread, perceived catastrophic potential, and the inequitable distribution of risks. An unknown, novel risk can be defined by the degree to which a hazard is judged to be unobservable, unknown, new, and delayed in producing harm (Fischhoff et al. 1978 ).

2.3.3 Affect and Worldviews as Orienting Dispositions in Risk Perception

Peters and Slovic ( 1996 ) expand on the psychometric paradigm by hypothesizing that risk perception also corresponds to worldviews and affect-laden imagery in terms of orienting dispositions. Worldviews are defined as generalized attitudes toward the world and its social organization. Affect is an emotional impulse (for example, dread) that is triggered by external events and/or internal psychometric attributes. How a person feels about a hazard, that is, his/her affective reaction, influences risk perception (Raaijmakers et al. 2008 ).

Religion, education (knowledge), and information influence worldviews and/or affect. Rogers and Prentice-Dunn ( 1997 ) highlight information as a factor that initiates cognitive mediating processes with regard to the possible responses to the perceived risk. Messner and Meyer ( 2005 ) claim those having little knowledge about causes of natural hazards have a lower risk perception. The resulting orientating disposition guides people in hazard situations and influences their risk perception (Dake 1991 , 1992 ). However, questions of ancestral belief (faith) and religious perceptions are rarely addressed in the context of risk perception, although some 6 billion people belong to a religion. Yet, taking precautionary action is determined by a wide variety of beliefs and practices (Schipper 2010 ). Education in combination with information about risk usually changes affect and worldviews and thus influences risk perception (Raaijmakers et al. 2008 ).

2.3.4 Socioeconomics, Demography, and Gender in Risk Perception

Socioeconomic aspects, such as owning rather than renting a house or an apartment is usually connected with a higher level of risk perception (Qasim et al. 2015 ) even if this relationship cannot be generalized (Kellens et al. 2011 ). The role of demographic factors (for example, age, education) and gender in risk perception is also debated. Empirical evidence suggests that demographic factors influence risk perception (Peakcock et al. 2005 ; Bang 2008 ; Botzen et al. 2009 ; Kellens et al. 2011 ; Yu et al. 2017 ) but there is also evidence against it (Plapp and Werner 2006 ; Bronfman et al. 2016 ). The uniqueness of many hazards and the case-specificity emanating from place-based studies appear to generate contradicting empirical outcomes, emphasizing the need for more empirical studies.

Income diversification has been identified as a major risk reducing, ex ante strategy employed by the rural poor, such as farmers and women in large parts of sub-Saharan Africa (Seo 2010 ). However, as many of the income sources of a diversified portfolio remain tied to the major source of income and the well-being of the community, any shock that hurts the major income source and the community can place the diversified income in jeopardy. Molua ( 2011 ) states that a disaster that creates farm yield losses and farmland damages can have devastating impacts on all sources of income in Cameroon. Peacock et al. ( 2005 ) found in their U.S. hurricane perception study that people with low income and low educational attainment, women, and ethnic minorities tended to perceive risk higher than the opposite socioeconomic groups. There may be interaction effects between low education and low income, between low education and specific ethnic groups, or between low income and women. The emerging vulnerable socioeconomic situation may make them more susceptible to perceiving potential hazards as more perilous (Mertens et al. 2018 ).

2.3.5 Culture, Social Construct, and Risk Perception

Culture and social construct explain how people perceive and understand risk. Culture, for instance, provides socially constructed myths about nature (Dake 1992 ). As mentioned by Wildavsky and Dake ( 1990 , p. 42), the cultural theory of risk can “predict and explain what kind of people will perceive which potential hazards to be how dangerous.” According to the cultural theory, risk perception is related to the way of life, which is linked to the “cultural bias” and “social construct” of an individual (Oltedal et al. 2004 ). Cultural biases are defined as shared values and beliefs, that is, as worldviews corresponding to different patterns of social relations. Cultural theory expects a “strain to consistency” in individuals but recognizes that different cultural biases may dominate different parts of people’s lives (Johnson and Swedlow 2021 ). Grid and group are two important dimensions for identifying social relations. Johnson and Swedlow ( 2021 ) summarize that grid allows to understand the extent to which relations are prescribed (for example, by rank, role, and gender); and group the extent to which relation patterns are bounded (for example, us versus them). Social relations are defined as one of five patterns of interpersonal relationships, namely hierarchical, individualist, egalitarian, fatalist, and hermit, a zero grid and group type (Dake 1991 ). However, the empirical support for this theory has been surprisingly meager, and even less from developing countries (Oltedal et al. 2004 ; Johnson and Swedlow 2021 ). Social categories, for instance, hierarchy in the form of prescribed leadership positions (for example, elders, household heads) may perceive risk differently than other social categories. In addition, the ambiguity regarding gender may be a consequence of the phenomenon of “cross-cultural, cross-gender reversal,” implying “an influence of gender on risk perception [stemming] from cultural and social factors” (Lechowska 2018 , p. 1353).

2.3.6 Trust and Risk Perception

Trust is a necessary prerequisite for dealing with hazards, especially considering the limits of risk perception as a function of awareness and preparedness (Lechowska 2018 ). Oswald ( 1994 ) distinguishes five types of trust, among them trust in friends ( Freundschaftsvertrauen ) and trust in strangers ( Fremdvertrauen ). Trust in friends assumes goodwill; this is not a necessary aspect when it comes to trust in persons external to one’s own social construct. In developing countries, trust based on friendship is often observed among members of self-help and mutual aid groups. State and local government authorities and other potential sources of support and information are perceived as outsiders. Nevertheless, it is plausible to assume that trust based on friendship as well as trust in outsiders such as public authorities is influencing risk perception, especially if own knowledge and information about the hazard is low (Wachinger et al. 2013 ). The more knowledgeable an individual is regarding the hazard risk, the more he/she will make own decisions and rely less on others such as friends and/or strangers (Siegrist and Cvetkovich 2000 ).

In the context of infrequently occurring hazards, however, people may depend more on information by persons outside the own social construct. If trust in external sources of support and information is weak, on the one hand, individuals feel more at risk (Bronfman et al. 2016 ). A high degree of trust, on the other hand, can lower risk perception and reduce the intention to take own precautionary measures in light of possible future hazardous events (Paton 2008 ). Trust in authorities may thus potentially create a false sense of safety (Lechowska 2018 ).

2.3.7 Location, Experience, and Risk Perception

Individuals may have had different hazard experiences that may be linked to their place of living (Messner and Meyer 2005 ; Bradford et al. 2012 ; Yu et al. 2017 ). Peacock et al. ( 2005 ) argue that location is an under-examined factor in explaining risk perception. Wachinger et al. ( 2013 ) distinguish between “direct” and “indirect” hazard experiences. A direct experience is, for example, seeing and experiencing the hazardous event with one’s own eyes. Indirect experience is external, for example, hearing of an event through the media or other people. However, having been living (for a longer period) in a (natural) disaster zone and having direct experience is claimed to be one of the most important factors of risk perception (Bustillos Ardaya et al. 2007 ; Botzen et al. 2009 ; Kellens et al. 2011 ; Wachinger et al. 2013 ). Individuals with direct hazard experience may find it easier imagining a similar, future hazard and, therefore, may perceive a higher risk than individuals without similar experience (Botzen et al. 2009 ). People who were directly affected by a hazard may therefore decide to better prepare for future hazards (Siegrist and Gutscher 2008 ). Previous hazard experience can, however, also lead to a lower perceived risk associated with such an event in the future, with former victims assuming that they are less amenable and less vulnerable to future events and their negative impacts (Halpern-Felsher et al. 2001 ; Peacock et al. 2005 ).

Obviously, experience is linked to location, length of residence, and age. People who have resided in a disaster zone longer and/or older persons often have more hazard experiences than those with shorter residence or younger persons. The results regarding length of residence imply a positive but weak correlation with risk perception (Bustillos Ardaya et al. 2007 ). Kellens et al. ( 2011 ) report that elderly people have a higher risk perception. Botzen et al. ( 2009 ), Peacock et al. ( 2005 ), and Yu et al. ( 2017 ) find the opposite.

Furthermore, the type of hazard experience is important in risk perception. Even if all individuals in an area affected by a natural hazard, for example, a flood, claim to have directly experienced the flood hazard, not all of them might have experienced harm or loss (Botzen et al. 2009 ; Wachinger et al. 2013 ). This could further influence preparedness (Rogers and Prentice-Dunn 1997 ). However, if the experienced hazard is infrequent and not severe, people may have a false sense of security. This may lead to misjudgment of the ability to manage the risk (Raaijmakers et al. 2008 ; Wachinger et al. 2013 ).

2.3.8 Protection Motivation Theory and Risk Perception

The protection motivation theory (PMT) is based on the seminal work of Rogers ( 1975 , 1983 ) and is delimited along cognitive and physiological constructions. It is grounded in health psychology but is meanwhile increasingly being used to explain protective behavior in the presence of natural hazards (Mertens et al. 2018 ; Wuepper et al. 2020 ). It has many points of contact with the first three theoretical paradigms discussed above: the mental models, the psychometric paradigm, and the orienting dispositions such as affects and worldviews. For instance, the theoretical paradigm of orienting dispositions such as affects and worldviews and the PMT refer both to (persuasive) information and messages, which can stimulate cognitive mediating processes and subsequent changes in risk coping and management behavior. The PMT, however, also mentions the importance of the individual’s personality or dispositional characteristics, that is, non-cognitive skills (Wuepper et al. 2020 ), which are discussed above under the heading “socioeconomics, demography, and gender in risk perception.” Interestingly, the PMT considers prior experience with hazard related disasters as a largely underappreciated factor initiating cognitive mediating processes (Rogers and Prentice-Dunn 1997 ). In the theoretical paradigm associated to “location, experience, and risk perception,” experience was found to go both ways. It can motivate former victims of disasters to better prepare for future hazards, but it can also give them a false feeling of security. Bonß ( 1997 ) gives reason to this phenomenon based on the selective perception of actual insecurity: The only way to feel safe is to assume that nothing is going to happen. However, the PMT emphasizes the change in attitude and behavior due to the motivational role of risk perception (termed threat appraisal in PMT) and the associated threat of suffering (Rogers and Prentice-Dunn 1997 ; Floyd et al. 2000 ). The central message of Rogers and Prentice-Dunn ( 1997 , p. 113) is that risk perception motivates people to take protective actions in order “to avoid the unpleasant consequences of not taking those actions.” The central factors highlighted in the PMT are the severity of the potential disaster, the vulnerability of the threatened, the individually perceived self-efficacy to respond to the thread, and the belief that the recommended action to manage the threat is effective (Rogers and Prentice-Dunn 1997 ). Two distinct but related processes occur in response to the risk and to prevent the negative impacts: the cognitive process and the protective responses (Delfiyan et al. 2021 ). Thus, the PMT is closely linked to the perceived adaptive capacity of those at risk in anticipation of the next natural hazard and disaster (Wuepper et al. 2020 ). Adaptive capacity refers to an individual’s or group’s general ability to make adjustments so as to become more effective at dealing with the disastrous consequences of hazards (Birkholz et al. 2014 ).

3 Study Area, Data, and Parametric Model

The empirical data relate to Cameroon. Its geographical location, geological composition, tectonic history, and climatic zones make it one of the most exposed and affected countries by natural hazards in sub-Saharan Africa.

3.1 Study Area

The place-based study relates to the subdivision of Babessi and the rural town Babessi (around 25,000 inhabitants in 2015) in the Ngoketunjia Division in the Northwest Region of Cameroon (Fig. 1 a). Babessi Subdivision is situated in the Ndop Plain, a valley surrounded by a mountain chain consisting of Bamenda Mountain and the Oku Mountain, which is part of the Cameroon volcanic line (Fig. 1 b). The Ndop Plain is located between the latitudes 6°50’ N and 6°39’ N and longitudes 10°23’ E and 10°28’ E, covering a surface area of about 240 km 2 with a mean annual rainfall of 1,700 mm. The climate is equatorial with two seasons: a rainy season of eight months (mid-March to mid-November) and a dry season of four months. Torrential down pours ushered in by the monsoon winds characterize the rainy season, at times accompanied by destructive storms. The Ndop Plain is drained by the Nun River (Fig. 1 a) and its numerous tributaries. The Bamendjin Dam, constructed on the upper Nun River further down the Ndop Plain (Fig. 1 b), increases the risk of floods because the dam has caused the water table to rise in many areas of the Ndop Plain. This makes the subdivision prone to flash flooding. The situation is aggravated by the failure of the state to maintain and manage effective drainage systems (Aka Tangan et al. 2018 ).

Modified by Aka Tangan et al. ( 2018 , p. 215), adapted from Wotchoko et al. ( 2016 , p. 430)

Geographical location of Cameroon and the study area. a Location of Babessi Subdivision in Ngoketundjia Division, Cameroon and Africa; b Digital elevation model of the Ndop Plain. Sources a Aka Tangan et al. ( 2018 , p. 214); b

On 9 September 2012, Babessi was hit by a severe and fulminating flash flood. The flood lasted only for 30 minutes (18:30−19:00). Among other assets, 56 houses were destroyed. Furthermore, the flood washed away some 160 ha of crops (notably swamp rice, cocoyam, plantains, and beans) belonging to around 140 farmers. Human losses were serendipitously low, nevertheless, the flood constituted a disaster according to the definition of UNISDR ( 2009 ). Due to the good road access, humanitarian help quickly reached the town and was provided by state and nonprofit organizations (Balgah et al. 2015 ).

The Senior Divisional Office (SDO) of the Ngoketunjia Division, the territorial administrative agency of the Cameroonian government, treated only those families whose houses had collapsed as flood victims. Those whose houses had been damaged received little or no disaster relief from the SDO. Figure 2 displays pictures of some destroyed houses in 2012 and ongoing reconstruction in 2015. Disaster relief included stones for a sturdy foundation of the mud-brick houses (see Fig. 2 b). Furthermore, the beneficiaries of state disaster relief cash out reported that they had to repay FCFA 10,000 (about USD 17, about the wage of a day laborer for seven days) to the representatives of the SDO. They did not know why they had to repay part of the relief sum, but it is plausible to assume that the representatives of the SDO enriched themselves (Pohlmann 2015 ).

Source a Balgah ( 2012 ); b Pohlmann ( 2015 )

Three years after the 2012 flood in Babessi—reconstruction of destroyed mud-brick houses still ongoing. a Destroyed mud-brick houses after the flood in 2012; b Reconstruction of houses with stone-reinforced foundation ongoing, in 2015.

3.2 Data Collection and Sample

The impact of the flood related disaster on the Babessi residents’ livelihoods was first surveyed 6 weeks after the disaster in 2012 to assess the preliminary impacts of the floods on the livelihoods of victims. At the time, only 5% of all victims had fully recovered (Balgah et al. 2015 ). In summer 2015, Pohlmann ( 2015 )—as part of her Master’s thesis work—undertook a second survey in Babessi to study gender related differences with regard to vulnerability from natural hazards. The emphasis was on the Babessi flash flood of 2012. Her empirical work was supervised by two of the authors of this article, Balgah and Buchenrieder, and her survey data are the basis of this analysis. Shortly after the 2015 survey, on 14 September 2015, the area was hit again by a flood. On 5 August 2019, Ngoketundjia Division was hit by another flood. Footnote 6 Due to time and cost limitations, the 2015 sample was restricted to 138 (54.5%) flood victims and 115 (45.5%) non-victims. The composition of the sample was based on snowball sampling. Almost 70% of all respondents were worried of floods. As pointed out earlier in the discussion of the theoretical paradigms, especially the psychometric and the paradigm related to location and experience, living in a natural disaster zone, having direct experience, and dreading the hazard are important factors of risk perception (Bustillos Ardaya et al. 2007 ; Botzen et al. 2009 ; Kellens et al. 2011 ; Wachinger et al. 2013 ). Clearly, floods are not novel risks (Fischhoff et al. 1978 ) to the residents of Babessi.

A structured questionnaire was developed following Balgah et al. ( 2015 ). The first section of the questionnaire was devoted to collect demographic and socioeconomic data, the second section to the disaster experience and risk perception. Subsequent sections addressed occupation and income creating activities, time allocation, decision making, access to productive capital, and leadership roles. In total, the questionnaire contained 90 questions. The questionnaire was pretested and administered in a paper-and-pencil survey by trained local enumerators.

The types of natural hazards and disasters included in the risk perception assessment of 2015 were based on earlier qualitative research in Babessi. Balgah et al. ( 2015 ) had asked the inhabitants of Babessi “in addition to floods, which other natural hazards have been affecting Babessi town?” The qualitative interviews revealed storms with violent winds and landslides. In addition to these and based on their importance in terms of their frequency in developing countries (including Cameroon), droughts and earthquakes were added to the structured questionnaire. Furthermore, information was gained regarding the responses of humanitarian agencies, including the state (Pohlmann 2015 ). Whether or not the respondents had received public disaster relief in the aftermath of the 2012 flood was determined by referencing a beneficiary list obtained from the SDO.

3.3 Description of Variables

The target variable “risk perception” was assessed using a Likert-scale: “On a scale from 1−5, with 1 representing ‘not dangerous’ and 5 ‘highly dangerous,’ how will you rate the danger of being harmed by one of the following natural hazards: storm, drought, earthquake, landslide, and flood in Babessi?” Note that the words risk and danger are semantically related words (Boholm 2011 ). Nevertheless, while risk refers to a potential future loss as a consequence of a decision, danger is not conceived to be the result of a decision, but rather as a potential loss resulting from something external to the one affected (Luhmann 1993 ). However, Boholm ( 2011 ) empirically found that the frame element of “decision” is commonly realized in the term risk and danger. Furthermore, we are confident that the term danger captures the individual’s anxiousness with the consequences (Sjöberg et al. 2004 ). Therefore and because the general schooling level is low in most rural areas of developing countries (for example, only 25% of the respondents in this study had post-primary school experience), we used the ordinary language term danger to assess risk perception after having pre-tested the questionnaire. Table 1 summarizes the respondents’ risk perception of being harmed by one of these natural hazards. It is evident that the average perceived risk is highest for flood hazards (mean of Likert score is 3.86); 65% of the respondents perceive it as (highly) dangerous. Storm hazards are perceived as constituting an elevated danger too (3.57). This can be explained by the aforementioned heavy rainfalls in the neighboring hills of Babessi, which were identified as the main hydrometeorological reason for the flash flood. As one of the respondents said: “Sometimes, we have heavy runoffs here in Babessi town. These runoffs are frequent when rain falls in the hills that surround the village. This means that we are exposed to heavy runoffs, even if it does not rain in Babessi” (Martha, a female flood victim in Babessi).

Because flood hazards constitute more than 50% of all natural hazards in developing countries (World Bank 2014 ; Statista 2020 ) and because the respondents perceived flooding as most dangerous, the focus of the analysis here is directed towards flood risk perception. The risk perception parameters depicted in Table 2 represent a cross-disciplinary intersecting set that is based on the review of the dominant theoretical paradigms underlying risk perception research.

Table 3 summarizes frequency statistics of the predictors. The age of the respondents ranged from 15 to 100 years, averaging 41 years. The household size was 7.8, above the national average of 5.2 persons. More than half of the respondents (51%) were illiterate. The culture of self-help and mutual aid is widespread in sub-Sahara Africa, also in Cameroon. The absence of public social services, for the most part, motivates this privately organized provision of social assistance. This is reflected in the fact that about 72% of the respondents were active in a self-help or mutual aid group.

3.4 Study Limitations

This study has some limitations. First, data collection was carried out about three years after the flash flood in Babessi. This might cast doubt regarding respondents’ answers concerning their risk perception at the time of the disaster. Yet, Lindell et al. ( 2015 ) state that people remember events well that were personally relevant to them. This statement was confirmed during the pre-testing of the questionnaire in Babessi (Pohlmann 2015 ), during which the test-interviewees responded clearly. Therefore, we are confident in the analytical results of the collected data.

Furthermore, the primary data used here were originally collected to analyze gender sensitive vulnerability issues in the aftermath of the 2012 Babessi flood. Therefore, the data did not allow constructing psychometric, affective, or adaptive capacity predictors. Footnote 7 Nevertheless, there is a very good overlap with the predictive variables identified by Lechowaska ( 2018 ), who undertook an informative review of 50 empirical studies on flood risk perception in the last 20 years (see footnote 6 ). Furthermore, implications for the adaptive capacity to reduce the risk from floods are deduced from the risk perception discussion.

3.5 Data Analysis and Choice of Parametric Model

Although the questionnaire used by Pohlmann ( 2015 ) originally surveyed five categories of risk perception, the dependent variable was ultimately reduced to three categories (see Table 1 ) to coincide with the econometric specification, to have at least 25 observations per category of the dependent variable (Backhaus et al. 2016 ). The first two categories of the dependent variable (“not dangerous” and “slightly dangerous”) as well as the third and the fourth category (“more or less dangerous” and “somewhat dangerous”) were merged to “not or slightly dangerous” and “dangerous” respectively. Only 7.5% of the 253 respondents considered floods as “not dangerous.” Between 15 and 17% perceived flooding as “more or less dangerous” and “somewhat dangerous,” respectively. The original fifth category “highly dangerous” was maintained and represents the third category of the dependent variable (Table 4 ).

For multiple reasons, the multinomial logistic regression (MNR) was the choice of the parametric model. First, one can think of the MNR as an ordered model, which is based on the adjacent approach to comparison. Therefore, the MNR applies to the intrinsic order implied by the dependent variable. Second, the adjacent approach of the MNR is the preferred approach when individual categories are of theoretical interest and/or there is a middle category in the ordinal scale. Third, the MNR does not apply the proportional odds assumption to any of the predictors (Fullerton 2009 ). Let us consider, for instance, the predictor flood victim in 2012 (yes, no). It is plausible to assume that those who were not flood victims perceive floods as not or slightly dangerous. Given that the response to the risk perception of flood victims will not go through a sequence of stages (from “not or slightly dangerous” to “dangerous” to “highly dangerous”), the adjacent approach is the most appropriate for this type of ordinal outcome.

Generally, the MNR is used to predict the probability of category membership based on multiple predictors. The output from a MNR is typically presented as a series of comparisons with a single baseline group, which is here the category “not or slightly dangerous.” The model is summarized as follows:

Flood risk perception:

whereby $\alpha =$ intercept, $\beta =$ regression coefficient, ${X}_{i}=$ predictors, and $\varepsilon =$ error term.

The model building process involves several stages. First, predictors are examined for multicollinearity using the variance inflation factors (VIFs). The VIFs for all explanatory variables were below the conservative critical value of 3. Therefore, we can safely ignore the issue of multicollinearity. Furthermore, it is desirable to use a model that not only fits the data but is also specified with as few predictors as possible (Best and Wolf 2010 ). The number of observations per predictor in the model is 14, which is sufficiently above the recommended minimum of 10 (Vittinghoff and McCulloch 2007 ).

Second, although the MNR is robust against outliers, predictors were examined using appropriate diagnostics. Schendera ( 2014 ) suggests running separate binary logistic regressions. As the category “not or slightly dangerous” was selected as reference category, it was compared to the two other categories in two binary logistic regressions. The studentized residuals were saved and seven observations with a residual greater than ± 2 were marked as possible outliers.

Third, the MNR was run with all observations and without the observations containing possible outliers. If the accuracy rate of the model increases by at least two percentage points, Schendera ( 2014 ) suggests using the reduced model without the outliers. Since this was the case, we decided to use the reduced sample (see Table 4 ) in the MNR to avoid biased coefficient estimates or very large standard errors. Another nine observations were dropped from the regression because of missing data. Finally, we ran the reduced MNR using bootstrapping to obtain robust estimates despite the relatively small sample.

4 Perception of Flood Risk—Results and Discussion

In the following, the MNR is presented focusing on the three categories for respondents’ perceived flood risk: (1) “not or slightly dangerous”; (2) “dangerous”; and (3) “highly dangerous.” The first category functions as reference category. The MNR is based on 237 observations (see Table 4 ).

4.1 Model Fit

The regression diagnostics for the goodness-of-fit, except for the Pearson Chi-square, are all within standard range. The maximum likelihood ratio Chi-square of 274.639 is significant at the 1% level, indicating that the current model (compared to the null model) significantly increased our ability to predict risk perception. The Pearson Chi-square is significant, portending that the model does not fit the data well (Schendera 2014 ). Nevertheless, Bühl ( 2012 ) points out that these asymptotic tests assume large expected counts in the classification cells, which is not the case here due to the rather small sample size (see Table 5 ). Furthermore, this situation arises almost any time when having continuous predictors in the MNR. Therefore, it is recommended not to put too much confidence in the result of the Pearson Chi-square.

The pseudo R-squares range between 67.5% (Nagelkerke-pseudo R 2 ), 58.8% (Cox & Snell-pseudo R 2 ), and 43.3% (McFadden-pseudo R 2 ). A pseudo-R 2 between 20% and 40% can already be considered a good model fit (Schendera 2014 ). Overall, the model classifies 81% of all observations and between 71% and 87% of the predicted risk perception responses correctly (Table 5 ).

4.2 Discussion of the Flood Risk Perception

Table 6 depicts the MNR results with regard to flood risk perception. The discussion thereof is organized along the major paradigms theorizing risk perception.

4.2.1 Socioeconomics, Demography, and Gender

Ownership of crucial livelihood assets is connected with risk perception (Qasim et al. 2015 ). The coefficient for land ownership is positive and significant. Most households try to be at least partly self-sufficient with regard to food production; therefore land is an important asset. Obviously, a flood can cause severe damage to this asset, eventually lasting for more than one harvesting season. Interestingly, the risk perception of households that own a house is not unanimous. The coefficient is positive for the risk perception category “dangerous” and negative for “highly dangerous” (although only at the 10% level). This is in line with findings of Mertens et al. ( 2018 ) for Uganda. Households that had lost their houses in 2012 to the flood received disaster relief and rebuilt stronger houses with a sturdy stone base (Fig. 2 b). Qualitative research revealed that these households seemed now to assume that they were no longer vulnerable to future flood hazards. House owners are, however, 5.5 times more likely to perceive floods as dangerous instead of not or slightly dangerous. As pointed out earlier, victims whose houses had been damaged but not destroyed in the 2012 flood had received hardly any disaster relief to reinforce the foundations of their houses. Thus, they may still perceive their houses being at risk to future floods. These results are in line with the negative coefficient for those who have received governmental disaster relief in 2012, the odds of perceiving floods as “not or slightly dangerous” increase by around 19%. This may also reflect their positive experience with an earlier governmental disaster relief intervention. As mentioned by one flood victim: “Building a house is not easy. But seeing it destroyed by a flood is even more frustrating. I was very devastated when my house was destroyed by the 2012 [Babessi] floods. I am however very grateful to the stones given to us by the SDO. You can see for yourself that the foundation of my new house is very high. I am sure that any future floods will not affect my house again. But no one knows […] maybe the next one [flood] will rise above this foundation. I do not want to imagine what will happen” (Peter, household head and flood victim in Babessi).

The predictors “age” and “income is diversified” are significant for the flood risk perception “highly dangerous” only. The coefficient for age is negative. This indicates that with one year increase in age, the odds of perceiving floods as “highly dangerous” as compared to “not or slightly dangerous” is reduced by about 13%. Counterintuitively, households with a diversified income perceive the risk of floods being “highly dangerous,” 7 times more likely as compared to “not or slightly dangerous.” Income diversification is, after all, identified as a major risk management ex ante strategy employed by many rural poor in large parts of sub-Saharan Africa (Seo 2010 ; Gebre-Egziabher et al. 2018 ). Molua ( 2011 ) found, however, that many income sources of the rural poor remain tied to their major occupation and the well-being of the community they reside in. He further emphasized that a natural hazard and disaster such as a flood can damage the major income source and thus has a devastating impact on all sources of income. In other words, even a well-diversified income portfolio of the rural poor may still be vulnerable to a significant covariate risk, namely natural hazard-related disasters. Diversification of income normally involves being in contact with the outside world, for example, when being engaged in transportation or as a (petty) trader. Therefore, we may also tentatively interpret this result such that these persons are better informed through social interaction. This in turn may change the mental model such that risk perception is heightened (see also the results related to “household owns at least one phone”). The predictor “household size” is negative for both expressions of flood risk perception, but significant for “dangerous” only. With each additional household member, the odds that risk is perceived as “dangerous” (as compared to “not or slightly dangerous”) is reduced by 33%. Traditionally—and especially in rural areas where the educational level is lower—larger families have been considered as an effective risk managing mechanism (Sooryamoorthy and Chetty 2015 ). First, family members function as nonwage-earning workers and financial supporters, which possibly makes coping with disasters easier. Second, bigger families have greater opportunity to diversify their income sources, which may help in managing risks—not to forget the parental old-age risk. This may explain the result.

Contrary to our expectation, the predictor “sex” was not significant. With regard to the sex of the respondent, empirical results are mixed. A number of scholars contend that women tend to rate the risk associated to natural hazards higher than men (Ho et al. 2008 ). The plausible explanation given relates to the potentially greater vulnerability of women in combination with their lack of empowerment and resources. Pohlmann ( 2015 ) found no gender link, hypothesizing that the flood disaster did not discriminate between women and men in Babessi, Cameroon.

4.2.2 Mental Models

A literate respondent (46% of the sample), as opposed to an illiterate, is on average 95% less likely to perceive the flood risk as “dangerous” or “highly dangerous.” Moreover, the odds that literate individuals, in comparison to illiterate people, will perceive floods only as “dangerous” and not as “highly dangerous” is 42% higher. This is a strong indication for the power of education regarding mental models or worldviews (Lechowaska 2018 ). It could also point at an interaction effect similar to the one pointed out by Brody and Highfield ( 2005 ) for the U.S. but in reverse: literacy may be plausibly linked to higher income and thus a lower risk perception.

The predictor religion was only significant for the flood risk perception “dangerous.” The odds of Christians as compared to Muslims to perceive floods as “dangerous” rather than “not or slightly dangerous” are 6 times higher. Bang ( 2008 ) found a similar result when investigating the Lake Nyos disaster in Cameroon. This result could imply that Christians link natural hazard-related disasters with a punishing God, especially when a reference is made to the biblical flood narrative as Wagner ( 2010 ) suggests. The Muslim population may have taken a less fatalist position, namely “Allah helps those who help themselves” (Schipper 2010 , p. 388). Footnote 8 Nevertheless, in the specific case of Cameroon, it appears more plausible to interpret this result based on location. Christian and Muslim residential areas in Babessi are mostly divided in such a way that Christians rather live in the valley and Muslims in the hills, where the latter rear cattle. In this respect, the Muslims may have been indeed less affected by floods because of locational factors.

The predictor “household owns at least one phone” (83% of the sample) is significant for the flood risk perception “highly dangerous” only. This predictor was included in the analysis as a rough proxy of connectedness to the outside world and to information (for example, on natural hazards or disaster relief measures). It seems that being connected to information initiates cognitive mediating processes as indicated by Rogers and Prentice-Dunn ( 1997 ), and thus changes the mental model such that risk perception is heightened. Those with phones are 7.2 times more likely to perceive floods as “highly dangerous” rather than “not or slightly dangerous.” A phone also increases the chances of the owner to be informed of the effects of similar floods in other places, which can influence flood risk perception.

4.2.3 Culture and Social Construct

The coefficient sign for the social construct predictor “head of household” is positive. A head of household, as compared to a regular household member, is 14 times (respectively 11 times) more likely to perceive the flood risk as “(highly) dangerous” as compared to “not or slightly dangerous.” The hierarchical role of a head of household may entail being responsible for the well-being of his/her household members. Although the empirical support for the role of rank and role in risk perception was meager (Oltedal et al. 2004 ; Johnson and Swedlow 2021 ), our results show the opposite. The results related to self-help further substantiate this statement. In developing countries, especially in more rural regions, citizens often perceive the state as absent or reluctant to provide public goods and services. Therefore, the culture of self-help and mutual aid is widespread to increase community resilience. Community resilience is closely linked to disaster risk reduction (Schelfaut et al. 2011 ). The odds of an informal leader of a self-help or mutual aid group as compared to a person without a leadership function to perceive floods as “(highly) dangerous” rather than “not or slightly dangerous” are about 4 times higher. This result may be associated with their selective awareness of having a social responsibility for the group members. These traditional risk coping mechanisms, however, perform best when mitigating idiosyncratic risks (for example, health risks) but are usually overstrained with mass disasters, such as natural hazards (Schrieder 1996 ; Balgah et al. 2015 ). Therefore, it appears to be a plausible result that group membership in traditional self-help or mutual aid groups does not significantly influence risk perception because members of self-help and mutual aid groups realize that these types of protective action neither improve individual nor community resilience notably in the wake of floods.

4.2.4 Trust

Respondents who had benefited from governmental disaster relief after the 2012 flood displayed a lower likelihood to perceive floods as “highly dangerous.” This is a strong indication for the benefit of public transfers, which are resulting in more trust into the state and local government (even though corruption was reported). Nevertheless, trust in authorities may also create a false sense of safety (Bonß 1997 ; Lechowska 2018 ).

The predictor respondent received informal help was not significant. As mentioned above, self-help and mutual aid groups are functionally constrained when all group members are hit by a mass disaster and are simultaneously in need of assistance. This is in line with UNISDR ( 2009 ) stating that disasters disrupt the ability of affected communities to cope with the effects using own resources. Therefore, they may feel confident that their group membership buffers idiosyncratic risks but not mass disasters. Indeed, only about 25% of the respondents had benefitted from informal social transfers after the 2012 flood.

4.2.5 Location and Experience

Although one would anticipate that previous hazard experience leads to a higher perceived risk, our results rather support Peacock et al. ( 2005 ) and Halpern-Felsher et al. ( 2001 ). People with previous hazard experience may think this has made them less amenable and vulnerable to future events and their negative impacts. Victims of the flood in Babessi in 2012 show a substantially reduced likelihood to perceive the flood risk as “dangerous” or “highly dangerous” (minus 85% or minus 78%, respectively). Nevertheless, the odds for somebody who was a flood victim in 2012 as compared to not are 32% more likely to select “highly dangerous” as compared to “dangerous.” This result is consistent with the “number of disasters experienced from 2012−2015”; this predictor shows a negative sign too, albeit not significant. Mertens et al. ( 2018 ) found for Uganda and the risk of landslides that those at risk may fall into what has been called a “fatalism trap.” They fear the disastrous effect of the natural hazard but do not believe that something can be done about it. They continue to say that this finding is rather new to the literature on protective behavior in the presence of natural hazards, but not to the literature on the PMT as a whole. In our case study, it is possible that the construction of houses with higher foundations after the 2012 flood could have moderated the risk perception of victims too.

4.2.6 Implications for the Adaptive Capacity of those at Risk to Reduce the Risk from Floods

The Sendai Framework for Disaster Risk Reduction 2015−2030 recognizes that the state has the primary role to reduce disaster risk but that an all-of-society engagement is important for implementing risk reduction measures at the aggregate and the individual levels (UNISDR 2015 ). Obviously, there exist structural and non-structural mitigation measures against floods at the state and individual levels. While non-structural mitigation measures may be available to many of those at risk (for example, self-help, income diversification, and so on), only a limited number of the structural measures (for example, sturdy house bases, small surface farm dams, and so on) are technically or financially feasible for potential victims of floods in rural areas of developing countries such as Cameroon.

In line with the PMT (Rogers and Prentice-Dunn 1997 ), the decision of those at risk of flood hazards in adopting precautionary measures to protect themselves appears to be linked to four broad perceptual processes: risk perception, coping appraisal, disaster experience appraisal, and administrative measures appraisal. When risk perception and coping appraisal are high, (potential) flood victims seem to be more likely to engage in protective action (Grothmann and Reusswig 2006 ; Bradford et al. 2012 ). Results from Europe further reveal that risk perception is a reliable predictor in terms of motivating potential victims of floods to adopt measures that require small investments in the form of efforts and costs but not high levels of investment (Koerth et al. 2013 ). This is especially true in the contemplative stage as reported by Gebrehiwot and van der Veen ( 2015 ) for drought victims from Ethiopia. Delfiyan et al. ( 2021 ) support this notion with regard to drought risks in Iran, stating that it is important to provide effective but low-cost responses by suggesting precautionary measures that require little time and money. Yet, the response efficacy must be given, especially for disaster victims in action (Gebrehiwot and van der Veen 2015 ). However, the likelihood to invest in risk management measures decreases when the (potential) victim has high confidence in the state to take appropriate action (Grothmann and Reusswig 2006 ).

Our results suggest that the majority of those at risk from floods are organized in self-help and mutual-aid groups. However, this non-structural risk mitigation measure is not very efficient for place-based mass disasters such as floods. At the same time, formal insurance systems are absent. Given that the response efficacy vis-á-vis floods of self-help groups could be improved, for instance through regional integration, there appears to be great potential for this form of risk management. Income diversification is known as risk management tool of those at risk. However, it may not function in view of mass disasters because many income sources of the rural poor remain tied to the major occupation and the well-being of the community they reside in (Molua 2011 ).

Here the state could proactively invest in regional development that is not affected by the place-specific hazard, for example, through creating low-threshold jobs in regional governmental offices. Furthermore, those responsible for designing and implementing flood risk information and management plans should ensure that they capture the knowledge from experienced flood victims (Bradford et al. 2012 ). The results of our study suggest integrating those with informal leadership roles in this process, as their risk perception is more pronounced due to their responsibility for the well-being of fellow residents. This further implies that flood risk information and management plans must be rather place-specific (Di Baldassarre et al. 2010 ). The Ministry of Territorial Administration and Decentralization together with the Directorate for Civil Protection are the responsible public authorities in Cameroon for disaster management. While the majority of the Cameroonian population owns a smart phone, there exists no emergency warning and information app yet. This could be a structural investment at the state level, which is low cost for those at risk (Khandaker et al. 2012 ).

5 Conclusion

The majority of natural hazards occur in developing countries and the related human suffering, especially in rural areas, is particularly high. However, studies on risk perception are rather few. In fact, perception of flood risk is often not available or overlooked when developing flood risk management strategies. This constitutes a disconnection between the people at risk from floods and the state, which has detrimental effects on community resilience with regard to flood hazards (Bradford et al. 2012 ). We make a contribution to narrowing this knowledge gap with an empirical case study of a flash flood disaster that occurred in 2012, in the rural town of Babessi, in the Northwest Region of Cameroon.

Heads of households and respondents with an informal leadership function displayed a higher likelihood to evaluate flood disasters as (highly) dangerous. We associate this result to their hierarchical social role as provider, either in the household or in informal self-help groups. The selective awareness of the social responsibility for the well-being of a group of people may lead to a heightened risk perception. Land is a most valuable asset in developing countries, where most households aim to maintain some degree of self-sufficiency with regard to food provision. A flood can destroy this asset and the crops growing on it not only in the short term but even for a longer period of time. Thus, it is not surprising that respondents who own land perceive the flood risk as (highly) dangerous.

Literacy was found to reduce the flood risk perception. Furthermore and counterintuitively, flood victims of 2012 display a substantially reduced likelihood to perceive floods as (highly) dangerous. Thus it seems plausible to assume that better educated people and people with prior exposure to a flood hazard may have developed different mental models and worldviews. It could also be indicative of a higher level of self-efficacy.

A number of factors turned out significant in only one of the two displayed risk perception categories. Worth highlighting is the effect of the public relief on risk perception. Respondents who had benefitted from governmental disaster relief in 2012 displayed a lower likelihood to perceive floods as highly dangerous in 2015. This is a strong indication of the benefit of public transfers, which is resulting in more trust in the state. Nevertheless, trust in authorities may also create a false sense of safety (Mertens et al. 2018 ). Thus, in addition to implementing policies that foster the adaptive capacity of (potential) flood victims in response to their risk perception, it is also important to avoid policies that could have a detrimental effect on a possible decision to adopt precautionary measures to protect themselves.

Some limitations abound in this study. There is ample evidence that flood risk management policies have been known to fail or be adversely affected when the subjective and contextualized nature of risk perception is overlooked (Bradford et al. 2012 ). First of all, while this article highlights the place-based context of risk perception, it could only indirectly link perception to private and public risk management strategies. Protective action differs however according to whether people are aware of their own risk or not. Depending on the degree of risk awareness in combination with a false sense of security, the impact of public information, warning, and risk management campaigns could fizzle out. Research on the explicit link between flood risk perception, public dissemination of related information, and risk management measures, could not only lead to increased trust in public authorities but also enhance the capacity to respond to floods and increase community resilience. Second, whether or not a household owns a phone was used in our study as a rough proxy for the connectedness of the household to the outside world and to information on flood risks. Yet, it is plausible to assume that those at risk of floods use various information channels and attach different degrees of confidence to them. What works and what does not in terms of information channels should be further investigated. Third, our research context, that is, risk perception of flood disasters in a rural town in a developing country is quite specific and thus, the generality of our results is unclear. Fourth, Schipper ( 2010 ) argues that risk perception with regard to climate change decisively determines the willingness to take precautionary action through a wide variety of beliefs and practices. Therefore, a differentiated analysis is necessary with regard to whether faith and religion can promote proactive behavior or fatalistic passivity. Fifth, we think it would be especially fruitful to investigate not only the flood risk perception of those at risk of a flood disaster but also of those who are not at risk and of the policymakers responsible for information and risk management campaigns. Finally, we could not determine the extent to which those at risk from flooding feel prepared or feel responsible for taking protective action (Bradford et al. 2012 ). Thus, it would be helpful to do a self-assessment study on how those at risk of flooding feel prepared for the next flood and feel responsible for taking protective action at individual and community levels.

Continuous research is thus necessary to derive more robust results regarding the factors influencing flood risk perception and resulting protective action in a developing country like Cameroon, where the occurrence of floods are escalating; and to identify tendencies that can facilitate flood risk management across multiple developing countries

An earlier version of this article has been published as a book chapter by Buchenrieder et al. ( 2019 ). The chapter is part of the edited volume “Risk - Thoughts on and into the Uncertain. Interdisciplinary Negotiations of the Risk Phenomenon in the Light of Reflexive Modernity”, in German language, published by Springer.

We would like to thank the anonymous reviewers for their careful reading of our manuscript, their insightful comments and constructive suggestions, which helped us to improve this article. All remaining errors are our own.

Countries with a gross national income per capita of less than or equal to USD 12,235 (of 2016 value) are defined as developing countries, above as high-income countries (OECD 2018 ).

We thus contribute to one of the priorities spelled out by the Sendai Framework for Disaster Risk Reduction 2015–2030: understanding disaster risk (UNISDR 2015 , p. 14).

In the context of risk perception research, some scholars (for example, Lechowaska 2018 ), use the terms worry or fear instead of dread. Kierkegaard ( 1991 ) distinguishes between fear and dread. Fear is directed towards an external danger (object-related affect) and dread is rather undirected or indeterminate. Obviously, the human affect associated with the risk perception of a natural hazard can be both, object-related and somewhat indeterminate.

A flash flood is defined as a “rapid inland flood due to intense rainfall […] with short duration” (CRED 2019 , https://www.emdat.be/explanatory-notes & https://emdat.be/glossary , accessed 5 Feb 2019).

Read more on the floods here: https://www.greenvision.news/after-babessi-bambalang-bangolan-baba-i-risk-flooding/ .

Lechowaska ( 2018 ) identified age, gender, physical location, flood characteristics, residence characteristics, size of consequence, range of impact, direct and indirect experience, demographic and socioeconomic characteristics, information, cultural-historical context, religious, and political context as risk perception factors.

It is worth mentioning that the same people, who believe in a Christian or Muslim God, also adhere to ancestral beliefs. Thus, one cannot literally differentiate between believing in an “important” god and believing in the ancestral “gods.”

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Buchenrieder, G., Brandl, J. & Balgah, A.R. The Perception of Flood Risks: A Case Study of Babessi in Rural Cameroon. Int J Disaster Risk Sci 12 , 1–21 (2021). https://doi.org/10.1007/s13753-021-00345-7

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doi: https://doi.org/10.1038/d41586-024-00835-w

‘Expert opinion’ and the figure are published under a CC BY 4.0 licence.

Kratzert, F. et al. Water Resour. Res. 55 , 11344–11354 (2019).

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Nevo, S. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.1901.09583 (2019).

Kratzert, F. et al. Hydrol. Earth Syst. Sci. 23 , 5089–5110 (2019).

Kratzert, F. et al. Sci. Data 10 , 61 (2023).

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Nevo, S. et al. Hydrol. Earth Syst. Sci. 26 , 4013–4032 (2022).

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Funding Tracks

Award Details

Topic Areas

Proposal Requirements

Funding Agreement

Submit a Track 1 or 2 Proposal
Submit a Track 3 Proposal

Call 5: Flood Ready Research and Data Publication

Call now closed, proposal q&a session.

Learn more about this funding opportunity by watching the recorded Q&A session here.

The Natural Hazards Center is pleased to announce a call for Flood Ready Research and Data Publication proposals in the social, behavioral, and economic sciences related to inland flooding.

Inland flood research is limited, so when extreme events—such as riverine flooding, flash flooding, and urban flooding—threaten communities, it is paramount that researchers collect and share perishable data with decision-makers. Such information can improve operational forecasts and warnings, minimize property damage, reduce injuries and deaths, and ultimately contribute to the collective good.

The goal of this funding call is to advance science through research focused on how diverse community members:

Perceive inland flood risks,
Prepare for inland flood threats,
Understand inland flood observations and forecasts,
Receive inland flood alerts and warnings,
Make protective action decisions, and
Respond to and recover from the impacts of an inland flood event.

This call, which is part of the Weather Ready Research Award Program , was made possible with the support of the National Oceanic and Atmospheric Administration (NOAA) Weather Program Office in partnership with the National Weather Service and the National Science Foundation.

NOAA is the primary authority for issuing official weather forecasts and warnings for life threatening hazards in the United States. The agency is taking major steps towards building a Weather Ready Nation .

As indicated, this call is exclusively seeking proposals related to inland floods, due to the limited amount of research conducted in this area. Proposals related to coastal floods and storm surge will not be considered. Proposals can focus on collecting new data and/or analyzing existing flood datasets, which can be found in several data repositories, such as DesignSafe , ICPSR , and Harvard Dataverse .

Applicants can apply for one or more of the following three tracks:

Track 1: Provides $2,500 to $10,000 for research on activities that take place before inland flooding , such as inland flood forecasting, gauging risk perception, preparedness, and mitigation.

Track 2: Provides $2,500 to $10,000 for individual researchers or teams to study inland flood activities (e.g., weather alerts and warnings, evacuation decision-making and behavior, inland flood impacts, displacement, rebuilding, recovery) during and after flooding . For this track, the flood event to be studied must have occurred on or after December 1, 2022 .

Track 3: Provides awards of either $1,250 or $2,500 for inland flood research instrument and data publication on DesignSafe , the Natural Hazards Engineering Research Infrastructure platform that provides data management and storage solutions for extreme event research. The $1,250 awards will support publication of one or more research protocols, instruments—such as surveys, interviews, or focus group guides—or observation protocols from a single project that is focused on inland flood-related research. The $2,500 awards will be reserved for those who publish a dataset and associated instruments and protocols for a single inland flood-related project in the social, behavioral, and economic sciences.

All proposals, regardless of the track or the funding range, must be led by a researcher in the social, behavioral, or economic sciences who works at an academic institution based in a U.S. state, territory, or tribal nation. Practitioners or additional research collaborators from other disciplines and outside the U.S. are welcome to join research teams. All applicants are strongly encouraged to propose inland flood research that is culturally relevant, ethically informed, and scientifically rigorous.

Proposals are due by 5:00 p.m. MT on Monday, March 11, 2024

Award details in brief.

All prospective applicants are encouraged to attend the Proposal Q&A Session on Tuesday, February 6, 2024 at 11:00 a.m. MT. This session will provide more information on this call.

Please review the submission guidelines for the three tracks of research activities that will be funded.

Proposals for Track 1 and 2 should be 5 single-spaced pages and must include a separate appendix with a reference list, budget, and budget justification submitted through an online form. Final reports are due Final reports are due on September 16, 2024 .

Available funds will support Track 1 or Track 2 awards in the amount of $2,500 to $10,000 each.

Proposals for Track 3 require the completion of a data or instrument publication form only. Data Publication Checklist and Data Publication Template are due on June 14, 2024 .

Available funds will support Track 3 awards in the amount of $1,250 or $2,500 each.

Applicants can apply for more than one track of activity, but a separate proposal should be submitted for each track.

Proposals for all tracks are due by 5:00 p.m. MT on Monday, March 11, 2024.

We welcome proposals on a variety of inland flood-related topics. These can include, but are not limited to, studies that accomplish any of the goals listed below.

Methodologies for Data Collection

Develop and test methodologies for systematically collecting data on diverse end users, such as emergency managers, operational forecasters, broadcast meteorologists, water resource partners (dam operators, flood planners, etc.), or other weather and water decision-makers. Of particular interest are projects that assess the challenges that these professionals face in perceiving, communicating, collaborating, and making decisions about localized inland flood information.

Develop and test methodologies to measure how the public receives, interprets, perceives, and responds to inland flood information, particularly in the context of protective action decision-making.

Develop and test methodologies to measure how the public plans for, adapts to, mitigates, and recovers from inland flooding events.

Identify, develop, and test methodologies to measure the effectiveness of impact-based decision support services that cover inland flood forecast information, technology, and tools.

Public Perception, Social Vulnerability, and Societal Impacts

Increase understanding of how diverse members of the public perceive inland flood risk when two or more types of uncertainty exist, such as in concurrent or cascading hazards. This may include uncertainty between variables, such as temporal versus spatial uncertainty of an inland flood.

Assess the needs, vulnerabilities, and challenges of historically underserved, economically marginalized, or socially vulnerable communities in relation to inland floods and flood risk.

Assess the societal impacts of an inland flooding event through the lens of combined interdisciplinary published datasets or dynamic datasets, such as traffic, mobility, insurance, or financial data.

Community Resilience and Preparedness

Build community resilience to inland flooding through educational outreach and awareness campaigns.

Examine strategies to ensure communities have access to information and tools to effectively plan for, respond to, and recover from inland flood events.

Economic Valuation

Conduct economic valuation studies to estimate the benefits of inland flood warning improvements.

Evaluate the potential economic advantages that historically underserved, or socially vulnerable communities can achieve when service equity gaps are reduced.

Proposal Requirements and Post-Award Deliverables

Please click on the links below to review detailed guidelines about proposal requirements and post-award deliverables, and learn how to submit a proposal for each track.

Tracks 1 and 2: Flood Ready Research
Track 3: Flood Ready Research Instrument and Data Publication

Award recipients must carefully read and agree to the following funding criteria:

The lead investigator , as designated in the proposal, must be from an academic institution based in a U.S. state, territory, or tribal nation . Other co-leads, project assistants, or local collaborators do not have to be affiliated with a university or located in a U.S. state, territory, or tribal nation—these individuals cannot, however, serve as the project lead and primary award recipient.

The lead investigator must have training and experience in the social, behavioral, and economic sciences . Collaborators from other disciplines are welcome.

Award payments can be distributed across team members as designated by the lead investigator (for example, 50% of the award sent to the lead, 25% to the co-lead, and 25% to a local collaborator). No more than three recipients can be designated for any one award, regardless of track.

Payments will be sent directly to the award recipients as designated in the budget to cover project-related expenses or time dedicated to data collection, analysis efforts, or the dissemination of results.

This award funding can NOT be sent directly to a university or other institutions , and there are no overhead or indirect costs associated with these funds.

Expenses may need to be paid out of pocket if fieldwork is involved and begins prior to receiving payment. Due dates will not be extended due to delays in payment processing.

Per tax compliance requirements, the University of Colorado Boulder will report payments to taxing jurisdictions when required. Individual payees will be issued any applicable tax forms directly from the University. Payees are responsible for any and all tax consequences related to payments they have received.

Individual recipients of these awards will be solely responsible for all tax reporting and ramifications . The Natural Hazards Center cannot provide tax advice . Awardees are allowed to include estimated taxes in their budget justification.

If you or one of your team members are a University of Colorado employee , please reach out to Katie Murphy at [email protected] prior to submitting a proposal, as the funding distribution has different requirements, including additional fringe and payroll tax considerations.

For award recipients who are non-U.S. citizens , the payment process may take longer and will require additional paperwork. All payments made to visa holders are submitted through the International Tax Office at the University of Colorado Boulder.

Once the award has been activated and the award agreement , tax forms , and IRB approval have been submitted to the Natural Hazards Center, researchers may begin fieldwork.

Please contact the Natural Hazards Center at [email protected] .

Acknowledgements

The Weather Ready Research Award program is based on work supported by the National Oceanic and Atmospheric Administration (NOAA) Weather Program Office through supplemental funding to the National Science Foundation (NSF Award #1635593). Opinions, findings, conclusions, or recommendations produced by this program are those of the author(s) and do not necessarily reflect the views of NOAA, NSF, or the Natural Hazards Center.

106 Flood Topic Ideas & Research Questions on Flooding

🏆 best flood topic ideas & essay examples, 📌 simple & easy flood essay titles, 👍 good essay topics on flood, ❓ research questions on flooding.

Sri Lanka Flood Disaster Preparedness From these findings, it is evident that floods are the major concerns for the disaster management center, with the recent damages being witnessed towards the end of 2012 and the beginning of the year 2013.
Floods, Technology and Price Ceiling in the Market From the graph, assuming that the equilibrium price in the fruits and vegetable market was EQ0, the floods destroy the products in the fields and this causes a shift of the supply curve to the […] We will write a custom essay specifically for you by our professional experts 808 writers online Learn More
Natural Disasters: Earthquakes, Floods and Volcanic Eruption This is due to the relationship between an eruption and the geology of the area. It was observed that the mountain swelled and increased in size due to the upward force of magma.
Great Barrier Reef: Flood Alleviation Solutions In the first presentation, solutions to protect the Great Barrier Reef, which is endangered from rising acidity levels due to methane extraction, were given while the second, third and fourth presentations focused on the measures […]
Floods in Los Angeles and Disaster Response The Los Angeles local government is set to respond and control the effects of floods. Therefore, the local government and citizens have set aside adequate resources to respond to the disaster.
A Climate Economics Issue: Increased Flood Risks There is a number of flood management plans in the United Kingdom for rivers where risks are known, such as the Anglian River basin.
The Louisville Flood Photo by Margaret Bourke-White The peculiarity of this photo is that it shows the contrast between the black people standing in line and the white ones painted on the placard.
The Devastating Flood of 1993: Lessons Learned In order to understand the causes and consequences of the flood that occurred in the summer of 1993, it is necessary to define the meaning of the concept of flood.
Ethical News Coverage: Indian Floods 2020 As part of the assessment of the consequences of reporting these events, it should be noted that the materials presented can attract public attention to help people in the affected areas, which is important for […]
Addressing the Threat of Flash Flood to Birmingham, Alabama The purpose of the work is to identify the key stages of threat addressing, including mitigation steps, preparedness and communication mechanisms, and response and recovery measures to address the outcomes of such disasters.
The Flood Stories in Different Cultures The scientific community recognizes that the oldest flood myth known to humanity is the Epic of Gilgamesh, which tells the story of Utnapishtim, who attained immortality by escaping from the flood on a ship.
Nova Killer Floods Documentary Review Flood is a phase of the water regime of the river, which is repeated every year at the same time of year, is characterized by the highest water content, increased and prolonged rise and fall […]
Floods in the City of Austin, Texas on October 30th, 2013 The catastrophic consequences of the devastation in Central Texas and, in particular, in the city of Austin, were caused by flooding.
Disaster Management in the Flood Scenario In such a case, the authorities and residents should adopt disaster prevention and preparedness strategies to minimize impact and adequately brace for the expected flood magnitude.
Flood Damage by Hurricane Maxine in Charleston The role of the mayor and his dignitaries is to determine the duration and level of use of resources by the city.
Local Hazard Mitigation: Floods While the federal government has been actively trying to reduce the scope of the problem for years, in the past decades, economic losses from floods have been growing. Overall, in the past years, NFIP initiatives […]
Theory of Disaster: Earthquakes and Floods as Examples of Disasters The second category is that of those people who put their focus on the effects of the social vulnerability or the disasters to the society or to the people who are likely to be the […]
Hydrology Methods: Flood Risk Management Digital spatial information modelling and the integration of the data and information used in the decision-support system illustrate the technical basis of the paper.
The Strategies of Flood Management However, it would be the most beneficial to implement these methods while planning the use of the land; for this reason, management is important.
A Flood Insurance Program in Canada: The Way to Protect Lives and Homes Floods are the major source of property loss: according to the analysis made by Munich, insurance companies do not want to take all the bills they get and ignore the majority of them.
Flood Effects That Occurred in July 2007 at Sheffield The report, therefore, entails in detail the immediate as well as the significant risks and losses caused by the flood, the factors contributing to the occurrence of floods, identification of all the agencies which were […]
Environmental Management: Floods Management Systems Considering the significance of environmental protection in the case of floods, the present report provides a detailed overview of such natural disasters in terms of contributory causes, impact, risks, and the role of environmental management […]
Minimizing Flood Fatalities in Canada The main goal of this study is to compile more details in regarding flood fatalities in Canada which may be useful in avoiding and preparing for flood related disasters.
City of Jeddah’s Flood: Cause and Disastrous Effects Jeddah is a city in Saudi Arabia found in the western region.and the it is a flat, low- lying ground next to the Red Sea.
Great Flood in Mississippi River Basin: Major Factors Mississippi River, the longest river in the United States and, with its extensive offshoots, is one of the most important river systems of the world.
Floods: Structural vs. Non-Structural Solutions The occurrence of hazards disorients the lives and experiences of many people. The selected community can mitigate this hazard through the use of non-structural and structural solutions.
The Ancient Near East: Civilization of Mesopotamia and Great Flood The Great Flood in Genesis and the Epic of Gilgamesh both depict the flood, the boat, the God of gods, and persons responsible for preserving humanity.
Flood Disaster Recovery Plan and Stakeholders The scope of this document: responsibilities, major hardware and software procedures, disaster response, testing of the recovery plan. The purpose of this disaster recovery plan is to provide detailed guidelines to all the stakeholders when […]
Gavin Flood’s Comparative Religion Studies In essence there is need to carry out more research in this field in order to be able to establish the role and the importance of religion in the life of human beings.
Flood Mitigation Measures in the United States The mitigation measures for floods include the following; “control over rivers, establishing policies and legislation on the use of land such as terracing and assess to flood-prone areas”.
Climate Change: Floods in Queensland Australia Over the recent past, the issue of climatic change has raised major concern about the well being of the recent as well as the future generation. The rail lines were also destroyed the fact that […]
The Flood of San Antonio in 1921: Re-Evaluating the Effects, a Catastrophe Viewed Through a Different Lens However, the reconstruction of the city takes less time than the reconstruction of the environment destroyed by the flood, which is why the effects of the San Antonio flood on the environment must be reassessed.
Year of the Flood While the Geneva Convention on Human Rights has banned the use and development of biological agents as a means of warfare, thus sparing humanity the possibility of dying due to a virulent disease, the fact […]
The Midwest Flood of April to October 1993 The Midwest flood of April to October 1993 is arguably the greatest flood to have hit the United States in terms of coverage and duration.
The Similarities of The Epic of Gilgamesh and Noah & The Flood
The Story of the Flood- the Epic of Gilgamesh
The Flood Has Changed History Forever
Red River Flood of 1997 & The Breakdown of Collaborate Management
Viability of Green Roofs as a Flood Mitigation Element in the Central Region of Chile
Rising Tide: The Great Mississippi Flood Of 1927 And How It Changed America, By John M. Barry
Regional Flood Frequency Analysis in Tunisia: Identification of Regional Distributions
The Economics During And After Kerala’s Flood Disaster
Sustainability-Based Flood Hazard Mapping of the Swannanoa River Watershed
The Demand for Index‐Based Flood Insurance in a High‐Income Country
Understanding Flood Risk Decisionmaking: Implications for Flood Risk Communication Program Design
Who Should Pay for Climate Adaptation? Public Attitudes and the Financing of Flood Protection in Florida
Sea-Level Rise and Land Subsidence: Impacts on Flood Projections for the Mekong Delta’s Largest City
The Flood Of Media Attention On Brain Injuries
Spatial Variation in Flood Risk Perception: A Spatial Econometric Approach
The Debate Over the Idea of the Genesis Flood in Genesis vs. Geology, an Essay by Steven Jay Gould
The Affordability Goal and Prices in the National Flood Insurance Program
The Fallibility of Flood Warning Chains: Can Europe’s Flood Warnings Be Effective
Special Flood Hazard Effects on Coastal and Interior Home Values: One Size Does Not Fit All
Land Use Scenario Modeling for Flood Risk Mitigation
The Effects Of Flood Damage On Everyday Life
The Bible According to Mark Twain: Writings on Heaven, Eden, and the Flood
The Story Of The Flood, How Utnapishtim Tells His Story To Gilgamesh
The City Of Vanport And Its Struggle With Racism Before And After The Flood Of Vanport
The Importance of a Flood Free and Clean Living Community
The Significant Key Elements on Climate Change in Before the Flood, a Documentary by Fisher Stevens
Smoothing Income against Crop Flood Losses in Amazonia: Rain Forest or Rivers as a Safety Net
Technological Advancements and Flood of Immigrants in the Turn of the Century in Ragtime, a Novel by John Pierpont Morgan
The Different Versions of Flood Stories in Many Different Culture
The Flood Story in Genesis, the Epic of Gilgamesh, and the Flood Story in the Holy Quran
The Truth Behind Noah And The Great Flood
Why the National Flood Insurance Program Is Not Financial Viable
Risk Management Solutions For Flood And Earthquake Catastrophes In Romania
Urban Growth and Flood Disasters in the Coastal River Basin of South-Central Chile (1943–2011)
Regional Flood Frequency Analysis Using L-Moments for the West Mediterranean Region of Turkey
The Intricacy of Adapting to Climate Change: Flood Protection as a Local Public Goods Game
The Flood Accounts In The Epic Of Gilgamesh And The Genesis
The Theme of Ancient Flood in Genesis of the Torah and the Epic of Gilgamesh
The Differences In Gilgamesh, Atrahasis & The Deucalion & Pyrrah In Ovid Flood Myths
The Factors that Influence the Flood Hydrograph
The Godly Perspective of the Corruption of the World in the Story of Noah and the Flood
The Devastation Left by the Flood in Downtown Davenport
How Can You Survive a Flood?
How to Promote Resistance to Flooding During Rice Germination?
What Are the Different Techniques of Flood Forecasting?
What Are the Consequences of Floods in Vietnam?
Is Climate Change Leading To Extreme Floods?
Where Is the Biggest Flood in the World?
Are You Willing to Pay to Reduce Environmental Risks From Sewage Flooding?
How Do Floods Affect Food Security in South Asia?
Has Community Awareness of Flooding Improved in Boulder County, Colorado?
What Are the Physical and Human Causes of Floods?
When Was the Biggest Flood in Sri Lanka?
What Could Be the Causes of a Dam Breach Leading To Flooding?
What Are the Strategies and Practices for Urban Flood Protection?
Does Your Insurance Cover Flooding?
What Organisations Assist People and the Community During a Flooding Event?
What Is the Estimated Economic Cost of Coastal Flooding?
What Are the Steps Taken by the Government to Manage Disasters?
Does Keeping Gutters and Drains Clear Help Against Flooding?
How Do Drought and Flooding Affect the Development of Grain Yield?
What Are the Types of Measures of Flood Management?
Is Flood Insurance in the Netherlands Different From Other Countries?
What Is the Impact of Land Use Change on Flooding Areas?
How Pakistan Floods Linked to Climate Change?
What Is the Interaction Between Floods and Economic Growth?
How High Is Urban Flood Vulnerability in Guyana?
What Are Some Tips to Prevent Basement Flooding?
How Should We Interpret the Genesis Flood Account?
Are Flood Risks More Physical Than Human?
Does Water Quality Deteriorate as a Result of Severe Flooding?
What Is the Effect of Flooding Along the Mississippi River on the Gulf of Mexico?
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1. Introduction. Floods are listed among the most destructive natural disasters, causing damage to socioeconomic, built, and eco-environmental systems (Rahman and Khan, 2011).According to United Nations (UN) statistics, floods have claimed >20.4% of human lives, and a total of 19.3% damages with 2.3 billion people affected worldwide between 1990 and 2020 (Mind'je et al., 2019; Wahlstrom and ...
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A flood risk curve is useful for considering the wide range of possible flood scales when assessing flood risk reduction effects of flood risk reduction measures. Apel, Thieken, Merz, and Bloschl ( 2006 ) analyzed the relation between levee breaching upstream and the flood risk downstream of the river Rhine in Germany.
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The study of flood risk perception factors can be considered by using different paradigms. In an attempt to understand risk perception, two basic paradigms can be distinguished: rationalist and constructivist. The rationalist approach tends to focus on modeling, characterizing, and predicting behavioral results regarding various threats. According to the constructivist paradigm, threats are ...
Causes, impacts and patterns of disastrous river floods
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An estimated 5 million people worldwide were rendered homeless during the period 1960-2000 due to floods (Marchiori et al., 2012).Additionally, about 100,000 lives were lost annually due to flood events worldwide, adversely affecting over 1.4 billion people in the last decade of the twentieth century (Jonkman, 2005).Despite advancement in research and technology, flood continues to be the ...
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1.1. Introduction. The study explores the impact of floods on the socio-economic livelihoods of people in Sikaunzwe community in Kazungula District. The aim of this study is to provide a thorough understanding of the impact of floods on the socio-economic livelihoods and underlying causes of the community's vulnerability.
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Although risk perception of natural hazards has been identified as an important determinant for sound policy design, there is limited empirical research on it in developing countries. This article narrows the empirical literature gap. It draws from Babessi, a rural town in the Northwest Region of Cameroon. Babessi was hit by a severe flash flood in 2012. The cross-disciplinary lens applied ...
Artificial intelligence can provide accurate forecasts of extreme
An artificial-intelligence model provides accurate forecasts of flood events — even in river basins where there are no data available. ... The research is a great example of how technology can ...
STUDY OF URBAN FLOOD MODELLING USING GIS. A REVIEW
PDF | On Dec 20, 2020, Kainat Ali Rang and others published STUDY OF URBAN FLOOD MODELLING USING GIS. A REVIEW | Find, read and cite all the research you need on ResearchGate
Call 5: Flood Ready Research and Data Publication
Award Details in Brief. All prospective applicants are encouraged to attend the Proposal Q&A Session on Tuesday, February 6, 2024 at 11:00 a.m. MT. This session will provide more information on this call. Please review the submission guidelines for the three tracks of research activities that will be funded.. Proposals for Track 1 and 2 should be 5 single-spaced pages and must include a ...
(PDF) Flood Causes, Consequences and Protection Measures ...
those areas near coast like so uth eastern Sindh and Makran. bear the coastal floods due to tropical storms. 1) Floods history in Pakistan: In the history, Pakistan has faced various disastrous ...
106 Flood Topic Ideas & Research Questions on Flooding
Floods in the City of Austin, Texas on October 30th, 2013. The catastrophic consequences of the devastation in Central Texas and, in particular, in the city of Austin, were caused by flooding. Disaster Management in the Flood Scenario. In such a case, the authorities and residents should adopt disaster prevention and preparedness strategies to ...
(PDF) Flood Research in Bangladesh and Future Direction: An Insight
stated that 97.1 % of Bangladesh and 139.6 million people are at risk of confronting. frequent oods because of hindu kush himalay an river systems. Glaciers melting of. the Himalay ans region and ...
Residential flooding in Zambia from Remote Sensing and Geospatial
A methodology for locating flood hotspots easily and quickly has been proposed. The method applies remote sensing and geospatial analytical techniques to generate flood hotspots.