Together with Asia, Africa is the continent most affected by floods (UNISDR-CRED, 2015). Between 2000 and 2018, there were 698 flood disasters in Africa, killing more than 14 250, affecting 45 million people and causing USD 6.8 million of economic loss (EM-DAT, 2018). West African countries, such as Burkina Faso or Senegal, appear to be experiencing an increase in flood disasters due to population growth and the urbanisation of flood-prone areas (Di Baldassarre et al., 2010). Between 1990 and 2014, floods were responsible for 86 % of the economic loss from natural disasters in Senegal alone (Preventionweb, 2018). During that period, the years 2005, 2009 and 2012 were marked by severe urban floods, particularly affecting the capital of Senegal, Dakar, causing human casualties and impairing socioeconomic conditions (GFDRR, 2014). The country is facing enormous challenges in flood risk management, exacerbated by climate change (Douglas et al., 2008; Urama and Ozor, 2010), rapid and uncontrolled urbanisation, a lack of drainage infrastructure, and rapid changes in land use that worsen drainage patterns (Chen et al., 2015; Ahiablame and Shakya, 2016).

The government and local authorities of Senegal have tried several strategies to mitigate urban floods, such as developing emergency plans, relocating inhabitants and building water retention basins. However, two key aspects, required for these measures to work, have not yet been considered. First, it is necessary to understand the causes and characteristics of floods, and, secondly, the local population must be involved in the process of risk management. Information on the magnitude and intensity of flood events, as well as on processes controlling the flood, is at the core of flood hazard assessment and zoning (EXCIMAP, 2007). This fundamental information is scarcely available in the region (GFDRR, 2014; Sy et al., 2016). The absence of an organised data acquisition system during floods has led to the absence of a comprehensive catalogue on past flood events and consequently on flood hazard maps.

Without records of past events and without the possibility of capturing the temporal dimension in terms of the frequency of occurrence, accurate flood hazard assessment is impossible to achieve. Moreover, floods are not only triggered by natural factors, but they are frequently influenced by man-made processes (WMO, 2012; DAEC, 2016), which are not easily recorded by ground-based instruments (Townsend and Walsh, 1998) or remote sensing (RS; Sanyal and Lu, 2004). Consequently, new alternatives must be explored. Citizen science is a form of collaborative research involving citizens in scientific projects (Wiggins and Crowston, 2011). Citizen science has attracted much attention from scientists in many fields such as ecology (Dickinson et al., 2010; Silvertown, 2009), astronomy (Raddick et al., 2007) and more recently hydrology (Buytaert et al., 2014; Paul et al., 2018). Rapid advancements in various modern technologies – the internet, web 2.0, virtual globe, location-based services, social media, mobile devices, interactive geo-visualisation interfaces such as OpenStreetMap, Google Earth and Geo-Wiki (Fritz et al., 2009; Mooney and Minghini, 2017; Yu and Gong, 2012) – as well as the rise of participatory research characterised by greater user interactivity and collaboration, have increased the number of studies and subjects investigated by citizen science projects. The use of citizen science has also emerged in flood analysis in recent years. The existing works can be classified according to which phase of flood risk management they are dealing with, i.e. before, during or after the flood event. For example, Sy et al. (2019a) reviewed the use of citizen science in flood hazard assessments, discussing its potential to gather information needed to develop realistic scenarios and provide flood hazard parameters, such as extent and water depth, that could help understanding the hazard level at a site. Assumpção et al. (2018) focused on the role citizen science could play in flood modelling and demonstrated its value to provide data for informing, calibrating and validating flood models, particularly where data are scare. It is notable that most of the existing studies have dealt with fluvial flooding; fewer studies have considered pluvial or groundwater flooding (See, 2019). Moreover, none of those citizen science projects have studied the reconstruction of past events using citizen memory, unlike the field of wildlife conservation where Zhang et al. (2018) demonstrated the value of citizen data for mapping past phenomena that were not otherwise recorded.

The objective of this work is twofold: (1) retrieve flood extents and water depths for different past events and (2) determine whether citizens can clarify the causal chain of flood events. We also assessed the reliability of these data by comparing them against independent methods, such as remote sensing.

Our citizen science approach was applied to the suburbs of Yeumbeul North (YN), one of the municipal districts of Pikine in Dakar, Senegal, western Africa (Fig. 1). YN covers an area of about 9 km2. It is one of the most populated districts of Senegal, with 168 379 inhabitants (ANDS, 2015) and a population density of approximately 18 700 inhabitants km-2. YN is characterised by lowlands with elevation less than 20 m above sea level and is highly urbanised with more than 80 % of its territory covered with buildings, critical facilities and roads (Sy et al., 2016). It is one of the suburbs most affected by flooding. Figure 1 displays the state of the permanent water bodies (Lake Warouwaye and Lake Wouye), which existed before retention basins were implemented as a mitigation measure after the 2012 floods (GFDRR, 2014; Sy et al., 2016).

Administratively, YN is divided into 82 major neighbourhoods. In each of these, a delegate, chosen among the inhabitants of the neighbourhoods, represents the municipal administration (decree no. 86-761, Republic of Senegal, Republic of Senegal) (GDS, 1986). The delegate should be from the neighbourhood and at least 35 years old. One of the delegate's tasks is to inform the neighbourhood inhabitants about how to face disasters. In this paper, we refer to the delegate as a neighbourhood chief (NC; Tall, 1998), an appellation employed by the local population.

Flooding in this area is mainly due to runoff and rainwater, which are not absorbed by impermeable surfaces, made worse by rapid urbanisation and the ineffective drainage network and combined with the rise of groundwater at some locations. Therefore, our area is characterised by multiple types of floods. Flooding occurs during the rainy season, which usually starts in July and ends in October. The three events considered here occurred in 2005, 2009 and 2012. Their timeframe and the peak rainfall intensity are provided in Table 1. The timeframe was retrieved from the Emergency Events Database (EM-DAT, 2018), whereas the rainfall intensity values were registered at the station of Dakar-Yoff, located 20 km away from the study area.

In this study, we have used citizen science to retrieve information on three past flood events that impacted the region of Dakar during the past 10 years. Our approach provides quantitative information on water depth, helps retrieve the flood extents and provides insights into factors that aggravate the intensity of floods.

Our methodology consisted of a set of techniques designed to gather the most complete spectrum of information. These techniques are unusual in the field of flood hazard assessment, and we had to resolve some challenges associated with the time that has elapsed since the events and participants' understanding of maps. One technique is based on people's episodic memory; we used face-to-face interviews with neighbourhood chiefs, applying specific tools in order to limit external influence and memory distortion. The procedure was then completed by a site visit with each neighbourhood chief involved in order to consolidate the verbally provided information. The site visit is very important because the time elapsed between the oldest event and the date of this study is about 12 years. As time goes by, memories can become vague (Lacy and Stark, 2013). However, people who have experienced traumatic and stressful events, like floods, tend to retain a more accurate, detailed and time-persistent memory of the event (Sotgiu and Galati, 2007).

Another technique involved participatory hands-on mapping. Mapping can represent a challenging task for laypeople (Handmer, 1985; Żyszkowska, 2015, 2017), as they may have difficulties understanding and locating themselves on a map. Moreover, maps are usually constructed by applying standard rules of graphic semiology (Thomas, 2001) that does not necessary take into account the cultural background or knowledge of the citizen (Fuchs et al., 2009). Therefore, if a citizen has no experience in reading or producing maps, information can be incorrectly reported. To overcome this problem, we trained people on how to read a map and locate themselves to ensure they understood the map, and we explained what they should be doing and how to do it.

Developing the quality and reliability of citizen science data is a growing research field (Crall et al., 2011; Flanagin and Metzger, 2008; Silvertown et al., 2015). In our study, we developed different strategies in order to improve these two aspects. We decided to work with two different target groups according to the context and the purpose of the study. The objective was to check the consistency of information obtained from the two groups. If the same area is described as flooded by both groups, there is a good chance that the area was indeed flooded. Due to the social organisation of the Dakar region, we limited issues regarding source credibility (Flanagin and Metzger, 2008) by involving neighbourhood chiefs. Indeed, these chiefs are appointed by local citizens, according to the trust placed in them and on their long-lasting presence in the area. Usually, they have a good memory and good verbal abilities. Moreover, as a witness or sometimes as a victim, they were at the forefront of the flood scene, therefore representing a valuable source of information on the chain of events. The second group was composed of local representatives, selected with the support of local and well-implemented associations.

Identifying the chain of processes generating flooding is very important for flood hazard assessment (DAEC, 2016) as it enables analysis of more realistic flood scenarios. Citizens living in flood affected areas are not frequently included in post-event or flood hazard assessments, even though they could provide useful insights as they have a good understanding of their surroundings (Tran et al., 2009) and in-depth local knowledge. Our study demonstrates this as the neighbourhood chiefs identified both natural and man-made factors that contributed to flooding, such as the rise of ground water, the Lake Warouwaye overflow and the emptying of septic tanks.

In terms of flooded areas, the results obtained from the two groups of citizens are similar for each event, although some spatial differences can be observed regarding the extent. Reasons for the differences could be related to (a) a more in-depth knowledge of the neighbourhood and their surroundings by the chiefs, as they have the confidence (Tall, 1998) of the inhabitants and therefore have access to more detailed information and (b) the techniques used in mapping the areas. With neighbourhood chiefs, we used a two-stage procedure to retrieve the flood extent, involving hands-on mapping and GIS mapping in the field, whilst the local representatives only produced hands-on maps that were then digitised.

A good spatial agreement exists between flood extents determined from remote sensing and citizen science, with better agreement from the data provided by the neighbourhood chiefs. However, areas provided by remote sensing are smaller. This discrepancy can be explained by various factors. One could be the different spatial resolutions of the selected images, which varied from 0.5 to 2.5 m; the larger were probably not being small enough to capture all flooded areas (Grimaldi et al., 2016) at the scale at which we worked. A second factor concerns the different time lapses between images. Post-event images from Google Earth were captured at intervals from 1 to 15 d, and therefore they may not have captured the maximum extent. Furthermore, for the 2005 event, one image was obtained during the flooding, with the second image taken one year after with the assumption it was captured during a dry period. A third factor is related to technical limitations of the capability of optical satellites to detect flooded areas, which is reduced when clouds are present (Malinowski et al., 2017; Mallinis et al., 2013). A fourth factor could be linked with the heterogeneity of sensors used in this study. A way to remove this source of discrepancy would be to use satellite images obtained with the same sensor. Finally, the efficiency of the NDWI index used to detect water areas could be altered by noise (Xu, 2006).

One of the techniques used to retrieve water depths was inspired from studies expressing flood hazard levels on maps using a body scale (e.g. EXCIMAP, 2007; Luke et al., 2018). Therefore, quantitative data on water depth were retrieved using a proportion of the size of the human body borrowed from the field of physiology (Winter, 2009). These values represent an average (Drillis and Contini, 1966), since the length of human body segments depends on body structure (Contini, 1972), gender and racial groups, and therefore it could be a source of uncertainties. However, when comparing the two approaches used for water depth investigation, we find a fairly good agreement, with average differences less than 0.3 m, which is within the range of other comparisons between observed and simulated methods (Kutija et al., 2014).

Both involvement and motivation from citizens are necessary for the success of citizen science projects (Rotman et al., 2012). As Facebook was one of the most used social media in YN at the time of the study (Sy, 2019a), we first created a page to interact with local citizens and motivate them to be part of the project. Secondly, we designed and presented the project in a way to convince contributors that their contribution will be beneficial for them and their neighbours. Thirdly, we worked with community leaders (Bénit-Gbaffou and Katsaura, 2014) and local associations to ensure a better acceptance of the project.

Citizen science requires involvement and time, compared to a remote sensing analysis which can now also take advantage of the free availability of radar images such as Sentinel (Malenovský et al., 2012). However, at the scale at which we worked, these images offer neither the required spatial resolution (Twele et al., 2016) nor information on the depth of the flood, which is a critical datum for flood hazard assessment that we were able to obtain with citizen science.

In conclusion, our study shows the potential of citizen science in retrieving quantitative and reliable information on past flood events, especially in areas where no or few records of past events are available. Our investigation strategy, involving two different groups of citizens, increases the reliability of the obtained data. Provided that the functioning of the society subject to floods is well understood, such an approach can be replicated in other parts of the world. Moreover, the citizens that have been involved in the various steps of this project have developed skills in flood data acquisition and an understanding of flood processes. They can thus better integrate into a decision-making process regarding flood risk.