# Linking human perception and scientific coastal flood risk assessment (Anlo Beach Community, Ghana)

Olusegun A. Dada, Donatus Bapentire Angnuureng, Rafaël Almar, Pierre Morand

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## To cite this version:

Olusegun A. Dada, Donatus Bapentire Angnuureng, Rafaël Almar, Pierre Morand. Linking human perception and scientific coastal flood risk assessment (Anlo Beach Community, Ghana). Ocean and Coastal Management, 2023, 243, 10.1016/j.ocecoaman.2023.106758. hal-04190810

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Submitted on 23 Jul 2025

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Distributed under a Creative Commons Attribution 4.0 International License
Linking human perception and scientific coastal flood risk assessment (Anlo

Beach Community, Ghana)

Olusegun A. Dada¹,², Donatus B. Angnuureng³, Rafael Almar¹ and Pierre Morand ⁴

¹LEGOS (IRD/CNRS/CNES/Université de Toulouse), Toulouse, France;² Dept of Marine Science & Tech, Federal

University of Technology, Akure, Nigeria;³ ACECoR, Centre for Coastal Management, SBS, University of Cape

Coast, Ghana;⁴ UMI SOURCE (IRD - UVSQ/PARIS SACLAY), Guyancourt, France

7

Abstract

Policymakers concerned with coastal management issues have recently focused their efforts on
how people perceive flood risk. Understanding the public's perception of risk has become an
essential component of contemporary flood risk management, as it provides a basis for designing
practical and effective flood mitigation strategies. This study was conducted in Anlo Beach, one
of the most vulnerable coastal communities in Ghana. It investigated household perceptions of
flood risk and examined them against independent physical measures that assess exposure. In
parallel, multivariate regression analysis was conducted to identify and establish the key factors
influencing household perceptions of flood risk in the study area. The results showed that two
variables, previous flood experience (data collected through the social survey) and factual
exposure (assessed through GIS measurements), played an important role in determining the level
of flood risk reported by households. In particular, the relationship between previous flooding
experience and perceived level of coastal flood risk was both positive and statistically significant.
Socio-demographic factors did not have a significant influence on risk reporting. We conclude
that perception variables collected through social surveys can be used as proxy indicators of
environmental risks when physical measures are not available. Biases based on the socio-
economic status of respondents may exist, but they do not outweigh information derived from
people's factual relationships with the environment. Further studies on the above factors would
support flood risk reduction measures in the study area and West Africa, particularly in light of
climate change.

Keywords: Anlo Beach- Ghana, social perception, coastal flooding, flood risk management,

flood risk

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31 **Introduction**
The coast, which is continually changed by shifting water levels, storms, waves, and currents, is home to a large portion of the world's population. While nature adapts to such fluctuations and changes rather easily, humans strive to maintain their status. In doing so, methods and tools have been developed over millennia to combat the sea and repair or extend the coastline (Costa et al., 2015). However, current and previous low-frequency, high-impact storms have illustrated the coastal hazard of erosion and flood threats faced by exposed coastal locations (World Economic Forum, 2023; Magnan et al., 2022; Oppenheimer and Alley, 2016; Jonkman et al., 2012). A good example of this was the catastrophic flooding West African countries suffered between 2008 and 2020 affected infrastructure, homes, agriculture and freshwater sources (OCHA, 2022). According to the United Nations Office for the Coordination of Humanitarian Affairs (OCHA), between 2001 and 2018 there were 676 floods (marine and river) and the cost of damages was estimated at around US$6.3 billion (OCHA, 2022). In 2021 alone, more than 1.2 million people were affected by floods in 13 West and Central African countries, resulting in 305 deaths, 2,563 injuries and about 378,000 displaced people. These floods also caused extensive damage to buildings, land and animals (OCHA, 2022).

Risk perception can be described as a subjective assessment by an element of society (at the level of an individual or a community) of the probability of hazard occurrence and its (mostly negative) effects (Bubeck et al., 2012; Becker et al., 2013; Costa et al., 2015; Koerth et al., 2013; Boyer-Villemaire et al., 2014). In a general case, the element under consideration (whether an individual or a community) has only an imperfect or partial command of the data and knowledge that would be necessary to formulate an accurate risk assessment with a known level of confidence. Nevertheless, information from people's perceptions of risk can be very enriching because they are drawn based on (i) a certain level of knowledge, albeit imperfect, about the mechanisms that generate natural phenomena (so-called 'traditional knowledge'), (ii) several lived experiences that are stored in memory (Brody et al., 2008) and (iii) cultural aspects specific to communities, such as representations. According to Kellens et al. (2011), the study of flood risk perception is related to human awareness, emotions and behaviour in the face of hazards.

There are two major reasons for studying people's perceptions of flood risk. First, how the public perceives risk (regardless of its accuracy or realism) is a major psychological factor that must be considered in order to succeed in a flood risk management policy and avoid the numerous failures observed in the past (Bradford et al., 2012). Characterization of public perception of risk is thus a
crucial information feature to consider when creating natural hazard policy and response management systems (Slovic, 2000).

The second reason for examining community perceptions of risk is to see if they may be used as a complement or alternative for long-term instrumental measurement data. Indeed, there are numerous cases where instrumental data do not exist because they were not collected over a long enough period or because they are not available for "fine" local scales. In such situations, alternative information must be sought, as well as the level of reliability, i.e., the degree of correlation with real phenomena, that declarative data acquired from a population survey can represent. This entails evaluating the consequences of the subjective bias associated with the processes inherent in the declaration of perception (taking into account what has been discussed above about the nature of the perception process). If a study can demonstrate that, despite certain biases, the quality of information collected by a population survey can be interpreted and used, such data can be validly integrated into a diagnostic study on the retrospective evolution of the environment in a given area or into a monitoring system, thereby implementing a community-based monitoring approach (Berkes et al., 2007).

In this light, the issue of the degree of agreement between the hazard level evaluations perceived by populations and those acquired from measurement data collected by experts is highlighted and must be addressed. Several studies have found that the amount of flood risk perceived by society differs from the level of flood risk assessed by experts (e.g., Dužíl et al., 2017; Dziaek et al., 2013; Ceobanu and Grozavu, 2009; Heijmans, 2001). In comparison to society as a whole, they are more realistic about the flood risk (Krasovskaia et al., 2001). The fact that people significantly underestimate the risk of flooding is a major problem and management concern, and this fact can be taken into account when designing risk management plans. As a result, it appears critical to understand the factors that influence the coastal population's perceptions of the magnitude of flood risk.

Concerning the aforementioned issues, the present study aims to assess globally the level of connection between the descriptions and risk assessments perceived by the surveyed population (the latter initially considered in an aggregated manner) and the seasonal and long-term trends as shown by the experts from the data collected by the measuring instruments. Second, the study aims to investigate the geographical (location) and socio-demographic characteristics that may influence inter-individual variations in risk perception among individuals
assessed in the same coastal area. This final section of the study may help to improve the
interpretation of the data in the future, or possibly redefine the composition of the survey
samples.

To achieve these objectives, the present study adopts a population-based survey to interview people of Anlo Beach, on Ghana's western coast, about their perceived environmental threats, especially floods. According to previous studies, Anlo Beach is one of the most vulnerable coastal areas in Ghana to erosion and flooding (e.g., Dada et al., 2022, 2023; Mutimukuru-Maravanyika et al., 2017; CRC, 2013).

## 2. Data and methods

### 2.1. Study site

The coastal community of Anlo Beach is located in the Shama District of the Western Region of Ghana (Fig. 1). It covers about 50.42 km² and lies at approximate latitudes 5°1' 30"N and 5°3' 5"N, and longitudes 1°34'30"W and 1°37'30"W. It is bordered to the north by mangroves and wetlands associated with the Pra River delta, which runs northeast to southeast. It is bordered to the north by mangroves and wetlands associated with the Pra River delta, which runs northeast to west of the community, and the south by the Atlantic Ocean. Hydrologically, the area lies within the plains of the Pra River basin, which flows directly into the Atlantic Ocean. The inhabitants of the Anlo Beach community are mainly fishermen, with an estimated population of about 4,500 people and 740 households. The main livelihoods of the Anlo Beach community are fishing (selling and processing fish), subsistence farming and petty trading. Other popular activities include sewing, hairdressing, catering and fabric dyeing.

Anlo Beach is partly located on a sandbank at the estuary of the Pra River. As the community is located on the dune and sandwiched between the wetlands and the ocean, it is highly vulnerable to flooding and erosion (Mutimukuru-Maravanyika et al., 2017). The topography of the area is below 10 m MSL. The community of Anlo and its surroundings/environments experience periodic flooding due to yet unknown cause but probably due to the annual rise in coastal water levels as a result of tidal and wind speed variations produced by tropical lows over the Atlantic, high flows of the Pra River, rainfall and the storage capacity of the wetland (DGRP-UCC, 2013). The community was completely isolated, and all access roads were submerged in 2017 following three cases of rapid erosion that occurred
simultaneously with flooding. These events prompted the Shama District Assembly to prioritise the resettlement of the community and consider it in its medium-term development planning (GMFADFC, 2020).

<figure><img src="image_3.png" /><figcaption>Fig. 1. Map of the coastal fishing community of Anlo Beach. Inset: Map of Ghana (Source: Britannica.com).</figcaption></figure>

## 2.2. *The population-based survey*

Households were sampled and analysed for this study. The sampling strategy was to randomly select one in five households from a list provided by community leaders, to ensure that the results of our survey would be close to those that would have been obtained if the entire population had been interviewed. Each household in the sample was interviewed using a questionnaire filled in with the information provided by the respondents (who are usually one of the partners heading the household). A sample of 156 respondents was obtained between September and October 2021.

The survey addressed questions related to flood risk. In general, the questions were largely structured around the manifestation of flood risk as observed by the respondent, its evolution in recent years, the impact of the risk on the individual, and the possible measures that are taken or could be put in place in the management of these potential or actual risks (Dada et al., 2023a; Morand, 2021; Louarn, 2020). The questionnaire also asked about the gender, age,
education level, length of residence and ownership of the respondents (Tables 1 and 2). To collect the data, the KoboCollect software (Kobo Toolbox, https://www.kobotoolbox.org/), installed on mobile phones, was used to conduct the interviews. The methodology used for this survey, as well as the questionnaire, the data set and further information on the and further information and are available in an online data set (Dada et al., 2023).

## 2.3 Geocoding of Respondents

We geocoded the respondents using the Global Positioning System (GPS) to locate the actual location of their houses on the land using longitude (X) and latitude (Y) coordinates, and then imported them into Google Earth (Fig. 2). The sample of 156 households represents a wide range of physical and geographical situations, at the scale of a coastal municipality. As each respondent is located in a geographical space, we were able to effectively use geographical factors and spatial analysis techniques to examine variables related to vulnerability to coastal flooding in the study area.

## 2.4 Creation of physical variables from the population survey

Based on the literature on the impact of coastal flooding, we measured and analysed the physical context variables associated with proximity to risk, relative height and frequency of flooding. Using Geographic Information Systems (GIS) techniques, we measured the distance between respondents' residences and the nearest point to the coastline or shoreline for inland flooding.

### 2.4.1. Indicators of the physical environment obtained by processing measurement data

Spatial data on physical phenomena were obtained from different sources, all independent of the population survey. These data were extracted from the nearest data points to the study area. For assessing seawater flooding, data on coastal water level (CWL) and its four components (i.e. tide - T; wave pattern - Ws, atmospheric pressure and wind-driven storm surge - DAC, regional sea level anomaly - SLA) in the study area were extracted from Dada et al. (2023b) and Almar et al. (2021). To assess river flooding, the river flow dataset was generated from the ISBA-TRIP hydrological model (Decharme et al., 2012, 2019). The obtained coastal water level (CWL) and river discharge data for the period between 2005 and 2015 were analysed for annual and monthly variations and trends. These data present the closest available climate variables to the study site.
As such, they provide baseline information on the hydrodynamic conditions of the Anlo Beach community and help to understand perceptions of flooding conditions among Anlo Beach households.

## 2.5. Methods of analysis

The perceived risk was derived directly from the questionnaire question: "Has the sea ever flooded your house or neighbouring houses?" and the two possible answers were "yes" or "no" (Table 3a). The answers to this question were compared with scientific or expert risk assessments. We estimated the percentage of respondents who lived in the risk zone and compared it to the percentage of respondents who perceived flood risks in the study area (Table 3b). We defined a coastal flood risk zone as an area at risk of flooding from storms and sea level rise. We determined this area based on the distance of buildings from the shoreline and the crest of the dunes, which averaged 50 m and 2.5 m, respectively. We then estimated the percentage of respondents who lived in the hazard zone and compared it to the percentage of respondents who perceived flood risk in the study area (Table 3b).

Other variables, concerning the socio-economic status of the respondents, their past experiences and their opinions or wishes regarding management, were also taken from the questionnaire and were used at different stages of the analysis. We used a regression model to determine the most predictive explanatory variables for the perceived risk of coastal flooding from a wide range of variables. Some of these variables represent descriptors of socio-economic conditions (gender, age, education level, length of residence and property ownership), and others represent (directly or as proxies) the objective physical level of exposure to flooding (proximity to risk, relative height and frequency of flooding). The variables which could have led to a risk of multicollinearity were excluded. All predictors were entered into the regression analysis at the same time. The Statistical Package for Social Sciences (SPSS v.19) was used to analyse the data.

# 3. Results

## 3.1 Characteristics of the sample

As Table 1 shows, the largest age group of respondents is between 18 and 50 years old, and there are more women (61%) than men. This can be explained by the fact that most of the men were away at work (fishing) during the surveys. About 11.5% of the respondents have higher
207 education as their highest level of education, while 26.3% have never received formal education.
208 However, more men have higher education than women. On average, 58.9% of the respondents
209 are young (i.e. between 18 and 50 years old), while the proportion of respondents over 50 years
210 old is 41.1%. Almost 53% of the respondents are homeowners and have lived in the study area
211 for more than 10 years (Table 2).

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<table><caption>213 Table 1. Socio-economic characteristics of respondents in the community of Anlo Beach</caption><thead><tr><th>Level of education</th><th>Woman</th><th>Male</th><th>Total respondents</th></tr></thead><tbody><tr><td><strong>Not at school</strong></td><td><strong>22.4%</strong></td><td><strong>3.8%</strong></td><td><strong>26.3%</strong></td></tr><tr><td>18-50 years</td><td>6.4%</td><td>0.6%</td><td>7.1%</td></tr><tr><td>&gt;50 years</td><td>16%</td><td>3.2%</td><td>19.2%</td></tr><tr><td><strong>Primary</strong></td><td><strong>23.2%</strong></td><td><strong>14.7%</strong></td><td><strong>37.8%</strong></td></tr><tr><td>18-50 years</td><td>18.7%</td><td>9%</td><td>27.5%</td></tr><tr><td>&gt;50 years</td><td>4.5%</td><td>5.8%</td><td>10.3%</td></tr><tr><td><strong>Secondary</strong></td><td><strong>13.5%</strong></td><td><strong>10.9%</strong></td><td><strong>24.4%</strong></td></tr><tr><td>18-50 years</td><td>12.2%</td><td>5.1%</td><td>17.3%</td></tr><tr><td>&gt;50 years</td><td>1.3%</td><td>5.8%</td><td>7.1%</td></tr><tr><td><strong>Tertiary</strong></td><td><strong>1.9%</strong></td><td><strong>9.6%</strong></td><td><strong>11.5%</strong></td></tr><tr><td>18-50 years</td><td>1.3%</td><td>5.8%</td><td>7%</td></tr><tr><td>&gt;50 years</td><td>0.6%</td><td>3.8%</td><td>4.5%</td></tr><tr><td><strong>Total</strong></td><td><strong>61%</strong></td><td><strong>39%</strong></td><td><strong>100%</strong></td></tr></tbody></table>

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<table><caption>215 Table 2. Demographic characteristics of respondents in the community of Anlo Beach</caption><thead><tr><th rowspan="2">Tenure status</th><th colspan="3">Duration of the residency</th><th rowspan="2">Total</th></tr><tr><th>&lt;5 years</th><th>5-10 years</th><th>&gt;10 years</th></tr></thead><tbody><tr><td>Owners</td><td>0.6%</td><td></td><td>52.2%</td><td>52.8%</td></tr><tr><td>Non-owners</td><td>5.2%</td><td>4.5%</td><td>37.5%</td><td>47.2%</td></tr><tr><td>Total</td><td>5.8%</td><td>4.5%</td><td>89.7%</td><td>100%</td></tr></tbody></table>

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## 3.2. The experience of flooding

217 When assessing the potential coastal flooding event (CFE) at Anlo Beach, a CFE is only
218 considered to have occurred when the coastal water level (CWL) values exceeded a threshold
219 value of 2.5 m (which was higher than the lowest dune crest height in the study area). As shown
220 in Figure 2, the dune crest height in the study area varies between 2 and 9 m. Most of the
221 interviewees who reported higher coastal flooding (CFE) are located on the western side of the
222 study area, with an elevation of less than 4 m (Figure 2).
223
The majority of respondents (84%) reported that their houses have been flooded, while 96% reported that they or their neighbourhood properties have been flooded (Table 3a). As shown in Table 3b, the houses of 24% of the respondents are located at an altitude of less than 2.5 m above sea level, while 52% of the respondents, respectively, lived at a distance < 50 m from the shoreline/riverbank. It is important to note that some of the respondents who currently live outside the risk zone (i.e., at higher elevations (>2.5 m) or more than 50 m from the shoreline) admitted that they had to move away from the shoreline to higher ground when the tidal waves became unbearable. This implies that in addition to exposure to flooding, their perception of flood risk was based on their previous experiences.

<table><caption>Table 3a. Flooding experienced by respondents in the community of Anlo Beach</caption><thead><tr><td>Variable</td><td>Respondents (%)</td></tr></thead><tbody><tr><td>Is the sea already flooded?</td><td></td></tr><tr><td>Your home:</td><td>84</td></tr><tr><td>Neighbouring houses</td><td>96</td></tr></tbody></table>

<table><caption>Table 3b. Comparison between the percentage of respondents living in a risk area and the percentage of respondents perceiving flood risks in the study area.</caption><thead><tr><td>Variable</td><td></td><td>Estimate (%)</td></tr></thead><tbody><tr><td>Flood risk based on perception</td><td>Respondents who perceived a risk of flooding</td><td>84</td></tr><tr><td rowspan="4">Flood risk based on exposure/geographical assessment</td><td>Respondents living within 50 m of the coastline</td><td>48</td></tr><tr><td>Respondents living within 50 m of the shore</td><td>6</td></tr><tr><td>Total respondents</td><td>52</td></tr><tr><td>Respondents living in areas less than 2.5 m above sea level</td><td>24</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>Fig. 2. Elevation contour, location, flooding extent during a typical extreme event in 2017 and flood experience (i.e., flooded or not flooded) of the respondents at Anlo Beach. As the figure shows, although some of the respondents moved to higher altitudes due to the increased intensity of the devastating tidal waves, they still based their perceptions on their previous experiences. Residents near the bank of the river also experience river flooding whenever the river bank overflow during heavy rainfall.</figcaption></figure>

### 3.3 Cause of flooding and perceived changes

Of all disasters, flooding is the most damaging and costly natural hazard for the community of Anlo Beach. Each year, the study area experiences severe flooding that has destroyed many properties. These periodic floods may be due to the interaction of several factors, including sea level rise and the flow of the Pra River. As shown in Table 4, flooding from the sea accounts for a significantly higher percentage, with the majority (89.7%) of households believing that when flooding occurs, some of the water comes from the sea, while 66.7% of respondents believe that at least some of the water comes from the river. In addition, respondents believe that flooding occurs in conjunction with periods of high water (80.1%), heavy rain (53.9%) and storm surges (23.7%). It has been reported that several houses have been washed away by destructive tidal waves, leaving many homeless (Gyekye, 2021).

These perceptions seem to correspond to the coastal water level (CWL) measured by a combination of different coastal processes: wave effects (R) due to a wave pattern and an oscillatory component (swash), the astronomical tide (T), the storm surge (DAC) due to atmospheric pressure, and the sea level anomaly (SLA) due to ocean circulation, mass transfer
from the continents to the ocean, and steric effect (Fig. 3 and 4; Almar et al., 2021; Dada et al., 2020).

As shown in Figures 3 and 4, the largest CWL, which can also be equated to the largest CFEs, was mainly dominated by wave effects, R (63.6% contribution), followed by tides, T (11.4% contribution), compared to the smaller negative contributions of sea level anomaly (SLA) and storm surge (DAC) (SLA: 1.1% and DAC: 10.8% contributions). Thus, the general perception that tidal surges are a major factor responsible for seawater flooding seems to be consistent with the CWL results for the study area. Furthermore, as shown in Fig. 5, over the past 10 years, the annual river discharge in the study area had a significantly negative (decreasing) trend, while the CWL indicated a slight positive (increasing) but not statistically significant (p≤0.057) trend according to Kendall's r test; Fig. 5). In general, the majority of floods in the study area are perceived by most respondents as coming from the sea (Table 4).

<figure><img src="image_5.png" /></figure>
Fig. 3: Annual mean coastal water level (CWL) showing the percentage contributions of the different CWL components, with wave run-up and tides having a larger proportion. Note: DAC = storm surge due to atmospheric pressure and winds; SLA = regional sea level anomaly; T = tides; R = wave run-up; CWL = coastal water level.

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<figure><img src="image_6.png" /></figure>

Fig. 4: Total coastal sea level, its components and river discharge in the study area at a monthly time scale. Note: DAC = storm surge due to atmospheric pressure and winds; SLA = regional sea level anomaly; T = tides; R = wave pattern; CWL = coastal water level.

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<figure><img src="image_7.png" /><figcaption>Fig. 5: Total coastal sea level and river flow in the study area on an interannual time scale.</figcaption></figure>

<table><caption>Table 4. Respondents' perception of the occurrence of flooding in the study area</caption><thead><tr><th>Variable</th><th></th><th>Respondents (%)</th></tr></thead><tbody><tr><td rowspan="2">When a flood occurs here, where does the water come from?</td><td>the sea</td><td>89.7</td></tr><tr><td>the river</td><td>66.7</td></tr><tr><td rowspan="4">Have you noticed that floods occur at the same time as the periods of?</td><td>Storm</td><td>23.7</td></tr><tr><td>High tides</td><td>80.1</td></tr><tr><td>Heavy rainfall</td><td>53.9</td></tr><tr><td>None of these events</td><td>0.64</td></tr></tbody></table>

### 3.4. Perceived and measured trend and frequency of coastal flooding

Most respondents (55%) understood that flooding had increased over the last ten years (Table 5a). About 46% of the respondents said that they had been flooded more than 4 times in the previous year, while about 32% said that they had been flooded up to 3 times (Table 5b). According to the respondents, flooding occurred mainly in May (12.1% of respondents), June (58.6% of respondents), July (89.3% of respondents), August (85% of respondents) and September (20.7% of respondents) (Table 6). In addition, 75.2%, 22% and 2.8% of respondents, respectively, believe that sea surges have occurred further inland, less inland and in the same place over the last ten years. In contrast, 90.9% of respondents believe that sea levels have risen in the last ten years (Table 7).
Although coastal flooding (CFE) estimated from observational data showed a decreasing trend in CFE from 2006 to 2015, there are more significant floods between 2008 and 2012 (Fig. 6a) and more significant than in previous decades (i.e., 1994-2005; Fig. 6b). Previous experiences may have influenced respondents' perceptions. As shown in Fig. 6, out of a total number of 74 CFEs occurring between 1994 and 2015, about 54 (73%) CFEs occurred between 2006 and 2015 and were frequent between May and September. The months with the highest number of CFEs were May, June and August (Fig. 7). However, respondents believe that July was the month with the most flooding (89% of respondents; Table 6); this perception may be due to the high river flow in that month (July) (Fig. 4).

To corroborate this statement, in July 2009, the Pra River overflowed its banks, destroying 60 and 78 houses in Krobo and Anlo Beach, respectively. Again, in July 2011, the river overflowed, displacing 625 people in Inchaban (Asiedu and Kusimi, 2021; Coastal Resources Centre, 2013). This suggests that the perception of flooding in the study area is linked to knowledge of past flooding over many years and is not limited to recent floods, but rather a long time series of floods over time.

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<table><caption>Table 5. Trend and frequency of CPEs in Anlo Beach according to respondents' perceptions.</caption><thead><tr><th>Variable</th><th></th><th>Respondents (%)</th></tr></thead><tbody><tr><td rowspan="3">a) Over the last ten years, has this phenomenon occurred more or less often?</td><td>More often</td><td>55</td></tr><tr><td>The same</td><td>6</td></tr><tr><td>Less often</td><td>39</td></tr><tr><td rowspan="3">b) How many times have floods occurred in the past year?</td><td>0</td><td>21.7</td></tr><tr><td>1-3</td><td>32.2</td></tr><tr><td>>4</td><td>46.1</td></tr></tbody></table>

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<table><caption>Table 6. Monthly variation of flooding according to the perception of the interviewees.</caption><thead><tr><th>Variable</th><th>Month</th><th>Respondents (%)</th></tr></thead><tbody><tr><td rowspan="13">What are the months when flooding is most common? (Answers for one or more months are accepted)</td><td>January</td><td>0</td></tr><tr><td>February</td><td>0</td></tr><tr><td>March</td><td>1.4</td></tr><tr><td>April</td><td>2.1</td></tr><tr><td>May</td><td>12.1</td></tr><tr><td>June</td><td>58.6</td></tr><tr><td>July</td><td>89.3</td></tr><tr><td>August</td><td>85</td></tr><tr><td>September</td><td>20.7</td></tr><tr><td>October</td><td>4.3</td></tr><tr><td>November</td><td>0</td></tr><tr><td>December</td><td>0</td></tr></tbody></table>

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<table><caption>Table 7. The extent of flooding and sea level during floods in the last 10 years</caption><thead><tr><th>Variables</th><th></th><th>Respondents (%)</th></tr></thead><tbody><tr><td rowspan="3">When the sea floods the land, is the water further inland than before?</td><td>Further inland</td><td>75.2</td></tr><tr><td>Same as</td><td>2.8</td></tr><tr><td>Less inland</td><td>22</td></tr><tr><td rowspan="3">When the sea floods the land, does the water level rise higher than before?</td><td>Higher</td><td>90.9</td></tr><tr><td>Same as</td><td>5.6</td></tr><tr><td>Further down</td><td>3.5</td></tr></tbody></table>

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<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

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<caption>Fig. 6 Coastal water flooding (CWF) at Anlo Beach a) between 2006 and 2015; and b) from 1994 to 2015.</caption>
<figure><img src="image_10.png" /><figcaption>Fig. 7: Monthly coastal flooding events (CFE) at Anlo Beach from 2006 to 2015.</figcaption></figure>

3.5. *Effect of some physical (localization) and socio-economic variables on inter-individual
differences in coastal flooding risk perceptions*

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Using the various types of data available, we combined the physical exposure variables to
flooding as well as the socio-economic variables in a multiple regression model (using robust
standard errors). This allowed testing the variables that have a statistically significant impact on
perceptions of coastal flood risk and more specifically to identify the possible effects of variables
of respondents' socio-economic conditions, which could help to explain and interpret some of the
inter-individual variability in responses. As shown in Table 8, three of the eight variables have a
significant impact on the perception of coastal flooding risk. The proximity or distance of
respondents' households to the coastline logically appears as the most significant predictor of
increased perception of coastal flooding risk (p <0.00). This is followed by the frequency and
intensity of coastal flooding (p < 0.01).

348
349
350
351
Residents living in low-lying areas (<2.5 m) near the coastline also responded
significantly higher to coastal flooding risk (p <0.05). However, the role of socio-economic
variables such as gender, age, education level, length of residence and property ownership is not
statistically significant (p > 0.05) and therefore did not prove to be a significant predictor of
coastal flood risk perception (Table 8). This suggests that perceptions of coastal flood risk at Anlo Beach are not, or only minimally, dependent on the socio-economic variables measured (Table 8). Nevertheless, it cannot be ruled out that a larger sample size could have led to the detection of slight significance for some demographic variables, in particular, age (p = 0.127) given that the sample size is quite small (n = 156).

The best predictors of perceived high risk of coastal flooding for a household in the study area were previous flooding experience, distance from the coastline and relative altitude from the coastline. The relationship between previous flooding experiences and perceived high risk of coastal flooding was positive and statistically significant (Table 8). This suggests that higher perceived flood risk is associated with previous flooding experiences. In addition, the perception of high flood risk decreases with distance from the coast, suggesting that perceived risk is related to the respondent's exposure to flooding, which is consistent with the basic assumption that people tend to perceive the risk of a hazard as higher the closer they are to it.

<table><caption>Table 8. Regression model to explain people's perception of high levels of coastal flood risk.</caption><thead><tr><th>Variable</th><th>Coefficients</th><th>Standard error</th><th>t Stat</th><th>p-value</th></tr></thead><tbody><tr><td>Type</td><td>-0.048</td><td>0.060</td><td>-0.791</td><td>0.430</td></tr><tr><td>Age</td><td>-0.255</td><td>0.166</td><td>-1.533</td><td>0.127</td></tr><tr><td>Level of education</td><td>-0.040</td><td>0.092</td><td>-0.436</td><td>0.664</td></tr><tr><td>Length of residency</td><td>0.094</td><td>0.144</td><td>0.651</td><td>0.516</td></tr><tr><td>Land ownership</td><td>-0.056</td><td>0.058</td><td>-0.953</td><td>0.342</td></tr><tr><td>Elevation relative to the shoreline</td><td>-0.109</td><td>0.049</td><td>-2.234</td><td><strong>0.027</strong></td></tr><tr><td>Distance of the household from the coastline</td><td>-0.209</td><td>0.057</td><td>-3.634</td><td><strong>0.000</strong></td></tr><tr><td>Previous experience with flooding</td><td>0.304</td><td>0.110</td><td>2.777</td><td><strong>0.006</strong></td></tr></tbody></table>

## 3.6. Analysis of responses regarding coastal flood management in Anlo Beach

The provision of defensive works such as coastal defence was suggested as a means of managing or mitigating flooding by some 64.4% of respondents, while relocation and the provision of early warning systems were suggested by 24.9% and 1.1% of respondents respectively as a means of preventing or mitigating flood damage. Coastal defence is often considered in areas with significant infrastructure and where the costs of protecting these investments or infrastructure are significantly lower than the costs of reconstruction, loss of economic activity, disaster response or
375 relocation of structures and activities (Mamo et al., 2018, 2022; Rangel-Buitrago et al., 2018; Pilon, 2002).
376

377 About a quarter (24.9%) of respondents expected the government to relocate people from
378 unsafe areas to safer locations provided that the government is willing to provide the
379 infrastructure for such a relocation programme and make the current location a fishing centre for
380 the inhabitants, which can be classified as a non-structural intervention.
381

382 **4. Discussion and Conclusions**

383 This study gives an overview of household views of flood hazards and risks in the Anlo Beach
384 community, one of Ghana's most vulnerable coastal communities. We start by looking at what we
385 can learn from the study about how people perceive flooding in general, and then we attempt to
386 find out how various elements could influence perception. We identify and explain the key
387 factors that influence household perceptions of flood risk in the study area using multivariate
388 regression analysis.
389

389 Our findings not only highlight the distinction between objectively measured risk and
390 perceived risk but also provide policymakers with critical information that they may employ to
391 mitigate the negative impact of coastal flooding on surrounding towns. We found that people's
392 perceptions of flood risk are substantially related to their geographical location and, as a result,
393 their level of exposure, which is consistent with previous studies (e.g., Glaus et al., 2020; Botzen
394 et al. 2009; Griffin et al., 2008).
395

This finding is consistent with the findings of Coquet et al. (2019), who conducted a
perception survey in four coastal municipalities in France and discovered that individual risk
perceptions accurately reflect near environmental conditions while being less sensitive to distant
environmental conditions. Our findings are consistent with prior research on environmental
hazards, which show that perceptions of natural and environmental threats are influenced by
proximity (e.g., Brody et al., 2004; Peacock et al., 2005; Cutter, 1996; Lindell, 1994; Kreutzwiser
et al., 1994; Farley et al., 1993). Similarly, we discover that perceived risk is proportional to the
area's elevation above the anticipated flood level. The higher the respondent's location in
reference to the anticipated water level, the lower their evaluated risk of flooding.
399
400
401
402
403

The influence of social and demographic factors on flood risk perception is not significant
in the present study, despite contradicting findings in the literature. Some studies, for example
405
406 (Ge et al., 2021; Zaalberg et al. 2009; Armas and Avram 2009; Chauvin et al. 2007), have found
407 that these types of factors have a significant impact on the public's perception of natural
408 catastrophes. However, Combest-Friedman et al. (2012) found that household proximity to a
409 climate hazard is the most important factor in determining the perceived risk to household assets
410 from the three coastal hazards studied, not the social and economic measures used in their study.

411 The only social variable that increases the perception of flood risk by people is previous
412 experience with the same type of event. This positive relationship is consistent with studies which
413 show that direct experience with risk makes it more likely (e.g., Paton et al. 2008; Miceli et al.
414 2008; Ruin et al. 2007; Domingues et al., 2018; O'Neill et al., 2016). For instance, Kreutzwiser et
415 al. (1994) found that perception of flood risk is strongly associated with previous flood
416 experience, proximity to the river and length of residence, after interviewing homeowners in the
417 floodplain.

418 Regarding the issues related to coastal flood management, the results of the study, which
419 show a high preference for the construction of physical defences, are in accordance with most
420 studies in the literature (Nunn et al., 2021; Kundzewicz et al., 2018; Gralepois et al., 2016;
421 Hegger et al., 2016). This may be because these types of solutions (such as groins and
422 revetments) have been deployed extensively along the Ghanaian coast previously and in more
423 recent years (Hagedoorn et al., 2021; Alves et al., 2021) and are therefore well known to
424 respondents. The negative environmental side-effects, well known to the experts (e.g., Jayson-
425 Quashigah et al., 2019; Appeaning Addo et al., 2018, 2015; Angnuureng et al., 2013), do not
426 seem to affect people's preference for this type of solution.

427 Relocation ranked a very distant second place (24%) as a measure suggested by
428 respondents for the management of flooding. This relatively low level of interest in relocation is
429 not surprising, as it is a very tough measure for local populations, particularly those dependent on
430 fishing. Many studies show that it is difficult to convince people to move and that in many cases
431 they only agree to do so when the level of risk becomes extremely high (Costas et al., 2015;
432 Giuliani, 2003). However, the Anlo beach site is somewhat unusual in that awareness-raising
433 campaigns have been carried out on this type of solution (see below), which perhaps explains
434 why the response rate, although low, is nonetheless much higher than zero. As suggested by the
435 minority (1.1%) of respondents, a flood warning and response system is another and the most
effective non-structural measure that can be considered to reduce the impact of flood risk in Anlo Beach. Respondents are probably not very familiar with this type of solution.

Structural measures through the construction of protective infrastructure and defensive structures (e.g., dykes) can be a good idea, but they can lead to the 'levee effect'. A situation in which, although floods are more frequent, people are less aware of the risk because of the structural measures put in place (Ge et al., 2021; Ridolfi et al., 2020). Therefore, to reduce the risk, flood-prone areas need more attention and non-structural interventions. Relocation could be an opportunity to correct previous planning errors. In this context, the government should encourage the relocation of people from flood-prone areas through compensation or incentives. These incentives can be financed through cost-sharing arrangements proportionate to the benefits to different levels of government in avoiding future flood damage. Most importantly, to reduce disaster-related losses, a public awareness campaign targeting the most vulnerable communities is needed to build the resilience of residents.

For example, the 2011 mid-year report on the Integrated Coastal and Fisheries Governance Initiative for the Western Region of Ghana, submitted by the Coastal Resources Center at the University of Rhode Island and funded by the United States Agency for International Development (USAID)/Ghana, indicates that previous attempts to relocate the Anlo Beach community to a new area were met with some resistance (Coastal Resources Center, 2011). However, after the involvement of the community in mapping spatial features and 'visioning' future land use and climate change, the Anlo Beach community leaders became aware of the imperative to relocate and expressed their willingness to be resettled. According to the said report, this community decision can be explicitly linked to the wave of awareness generated by the participatory land use mapping exercises (Coastal Resource Centre, 2011). This, therefore, means communicating about and raising public awareness of flood risks can improve understanding of landscape vulnerability and risks related to potential climate change impacts at the local level and facilitate informed land use decisions (Coastal Resource Centre, 2011). It further indicates that when stakeholders directly affected by management measures are included in the decision-making process, compliance increases and the need for enforcement decreases (Oubennaceur et al., 2022; Domingues et al., 2018).

We conclude that the perception variables collected by social surveys can be useful as they seem to be consistent with the coastal water level (CWL) measured by a combination of
different coastal processes. In an instance, perception variables regarding floods can be used as proxies for environmental risks, as respondents seem to have regular experience with flooding. In another instance (e.g., when subject to the "levee effect"), perception variables may be less reliable. Following previous studies (Oubennaceur et al., 2022; Philippenko et al., 2021; Bodoque et al., 2016), it is quite expected that people with flooding experiences have good knowledge and awareness of floods. In particular, knowledge of the seasonality of flood events is very good: the curve reconstructed by pooling all the responses is practically identical to that of the physical measurements recorded. Our study also suggests, as an additional finding, that the perception of the evolution of flood strengths or intensity could improve experiences over many years, which tends to show that floods are phenomena that are recorded in people's long-term memory. Nevertheless, the question remains whether people can reconstruct a precise timeline for the long-term evolution of floods. This could be the focus of future research.

The present study has important implications for the management and development of coastal flood preparedness and mitigation initiatives and programs. It reinforces the relevance of affective variables in flood risk perception and preparedness, which is useful for designing and implementing more sustainable coastal management plans. As suggested by Bradford et al. (2012), this could contribute in particular to better-targeted communication policies when it comes to building the capacity of communities to anticipate flood risks. However, the extent to which physical vulnerability influences public perceptions needs to be investigated further. Our research is primarily concerned with how people perceive risk. More research is needed on actual policy responses to coastal hazards.

## Acknowledgements

This study was carried out within the framework of the West Africa Coastal Area - mapping Vulnerability, Adaptability, and Resilience in a changing climate (WACA-VAR) project, sponsored by the OCEAN Department of the Institut de Recherche pour le Développement (IRD) and supported by the JEAI COASTS-UNDER-CONTROL between IRD and ACECoR. The first author is grateful to the management of the OCEAN Department of IRD for supporting his postdoctoral fellowship. He also thanks the staff of the Centre for Coastal Management-African Centre of Excellence in Coastal Resilience (CCM-ACECoR), University of Cape Coast, Ghana,
497 in particular the Director, Professor Denis Aheto, and the Associate Director, Dr Precious Mattah.

498 The authors thank the editor and the anonymous reviewers for their comments and suggestions.

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709

710 Acknowledgements

711 This study was carried out within the framework of the West Africa Coastal Area - mapping

712 Vulnerability, Adaptability, and Resilience in a changing climate (WACA-VAR) project,

713 sponsored by the OCEAN Department of the Institut de Recherche pour le Développement (IRD)

714 and supported by the JEAI COASTS-UNDER-CONTROL between IRD and ACECoR. The first

715 author is grateful to the management of the OCEAN Department of IRD for supporting his
716 postdoctoral fellowship. He also thanks the staff of the Centre for Coastal Management-African

717 Centre of Excellence in Coastal Resilience (CCM-ACECoR), University of Cape Coast, Ghana,

718 in particular the Director, Professor Denis Aheto, and the Associate Director, Dr Precious Mattah.

719 The authors thank the editor, Professor Timothy Frederick Smith, and two anonymous reviewers

720 for their valuable contributions to the first draft of the manuscript.

721