6.4 Local scale risk assessment for flashflooding in island countries


This Use Case uses the village of Dennery, Saint Lucia as an example to make the exercise tangible and reproducible. However, the data which is provided with the use case has been modified, complemented and generalized for the sake of this exercise. The results presented here cannot be used as basis for decision making. The authors do not accept any responsibility for the consequences that may result from any interpretation of this information beyond the purpose of this Use Case. 

 Use case intended for: Planner Spatial analist


Flash floods, risk assessment, vulnerability

Before you start: Use case Location: Uses GIS data: Authors:



Flash floods are floods that happen very suddenly and with little warning. They are caused by heavy or excessive rainfall in a short period of time, for instance thunderstorms or hurricanes, and occur within minutes or a few hours -  usually less than six -  after the peak of the rainfall event. Their severity may be aggravated by impermeable or saturated soils in the river's catchment and by the catchment's geometry. Flash floods can also occur after levee or dam failure, or after a sudden release of water by a debris or ice jam. Flash floods are very dangerous because they combine the destructive power of a flood with incredible speed and unpredictability and can be characterized as raging torrents that rip through an area sweeping everything before them. Another dangerous characteristic of flash floods is that they can transport large boulders and projectiles, such as uprooted trees and debris of destroyed buildings and structures, with high velocities downstream causing severe damage to whatever they collide with. The affected area is relatively small -  especially when compared to e.g. fluvial floods. However, these phenomena are not included in this use case. 

Flash flood risk assessment is the process to identify the harm that can be potentially caused by such a future flash flood event. This Use Case will give several examples how such an assessment can be done in a spatial manner, depending on the data that is available. To determine the risk of flash floods in a given area, one needs to 1) assess the nature and extent of the potential flash floods (flash flood hazard assessment) -  see also Methodology Book chapter 3.3 - and 2) evaluate the existing conditions of vulnerability (exposure and vulnerability assessment) -  see also chapter 7 of the Use Case book and chapter 5 of the Methodology Book. Together these two components determine the potential harm to exposed people, property, services, livelihoods and the environment -  see chapter 5.5 of the Methodology Book.

Risk assessment is an important step towards disaster risk reduction (DRR) planning and risk management. Qualitative risk assessment approaches can be used to prioritize areas that require attention (which areas are ‘worse off' than others), see examples 1 - 3, whereas quantitative approaches offer a basis for cost-benefit or cost effect evaluation of possible mitigation investments, e.g. by comparing the average annual loss before and after a proposed intervention, see examples 4 - 6. 

The pictures below give a good impression of the raging power of flash floods in the Caribbean. The picture on the left shows the river at Roseau, Dominica during hurricane Dean in 2007, the picture on the right was taking during tropical storm Erika in 2015 in Dominica.

1 Source: http://www.themontserratreporter.com


2 Source: Mike Theiss; Instagram: @ExtremeNature




The objective of this Use Case is to give a number of practical examples on the assessment of flash flood risk at local scale using a GIS depending on the availability of data. This Use Case is accompanied by a GIS dataset with data from the village of Dennery, Saint Lucia, with instructions for hands-on practice. The target group of this Use Case are GIS experts who's task it is to integrate the data that is generated or collected by other experts into a (spatial) risk assessment. 

Note: This use case requires data at local to medium scale, i.e. spatial data collected at a scale of 1:1000 to 1:10.000

Use case study area description:

Saint Lucia is highly exposed to tropical storms and hurricanes. These storms bring large amounts of rain that can result in flash floods.  Notable storms include Hurricane Allen (1980), Tropical storm (later Hurricane) Debby (1994), Hurricane Tomas (2010), and the trough system that battered the island on Christmas Eve 2013. These storms (and others) and the consequent floods caused enormous damage. For instance, the 2013 Christmas Trough resulted in a total loss of almost 100 million US$, which is an equivalent of 8.3 percent of the island's GDP. Hurricane Tomas in 2010 caused an estimated US$ 336 million in damage or 34 percent of the island's GDP (Fisseha, 2014). Such losses seriously impede the growth and development of the country. The 2013 event led to widespread flooding in the country and in Dennery village in particular. Several people had to be evacuated from their flood-inundated houses because the water depth was up to five feet (1.5 meters) and some bridges where damaged or overrun by the flood waters (source: WordPress.com, 2013). 

This Use Case presents a few examples of qualitative and quantitative risk assessment approaches for Dennery village. Which approach to use depends on the available data, on the preferences of the assessor and/or the requirements of the end user. 

Data requirements:

In general one can say that three types of data are necessary:

Flood hazard maps: These map contain the inundation depth information for different (at least three) flood return periods. Often the flood return periods are not so easy to define, so the return period of the causing event -  in the case of St. Lucia hurricanes and tropical storms -  is often used as a proxy. The return period is then obtained through a statistical analysis of the total daily precipitation amounts. Use Case 8.2 and chapter 2.3 of the Methodology Book give further details about this analysis. For the scale, please see Methodology Book, section 3.2. For local analysis, national scale assessments are not detailed enough; Assessments must be done at scales of 1:1000 to 1:10.000.

Exposure maps: These maps give the location of the assets that are (or could be) exposed to flash flood hazard. An introduction to the characterization of assets is given in chapter 5.2 of the Methodology Book. The Data Management Book gives further details about how to get e.g. building footprint maps (chapter 5.2), population distribution maps (chapter 5.3) and road maps (chapter 5.4).

Vulnerability: Vulnerability are the characteristics and circumstances of the assets (mapped in the expose maps) that make these assets susceptible to the damaging effects of the hazard. This term is further described in chapter 5.3 of the Methodology Book, exemplified in Use Cases 7.1 and 7.2. Vulnerability information is often  collected as a list of attribute parameters of the assets in the exposure map -  see also Data Management Book, chapter 5.2.


Especially the required vulnerability information is not well-defined as it depends on the hazard that is examined, on the type of assets that are mapped and on the resources available to carry out the exposure and vulnerability survey. In case of floods, buildings are most frequently characterized by the building materials (walls and roof), their use (residential, commercial, industrial, ..) and the number of floors. In detailed surveys one should also include mitigation and flood proofing measures, such as raised ground floor and the state of repair of the structure.

Analysis steps:

In these analysis steps we assume that certain layers of information are available. For instance, flood hazard maps are essential input. Chapters 3.3 and 3.4 of the Methodology Book provide background information regarding flood modeling and the flood hazard reports for Dominica, Grenada, St. Lucia and St. Vincent give an example on how to use flood model results for hazard assessment. Also the Super Use Case developed in this project may provide useful information about the modeling of hydrological and hydraulic processes in a river basin. Other layers may be absent or may not always contain the exact information that one would need for risk assessment. This is often the case for vulnerability data. As indicated in the previous paragraph this information is often linked to the assets in the exposure maps but it may be incomplete, absent or contain irrelevant attributes for our purpose. In this use case we will therefore present six examples: Three qualitative approaches based on flood hazard information only, and three qualitative approaches with one based on a combination of inundation depth and flow velocity, one on flood hazard information and exposure maps and a last one that integrates flood hazard, vulnerability and exposure (value) into a spatial flood risk assessment and that calculates the Average Annual Loss. All examples are linked to the GIS exercise and instructions are provided for the last two approaches. 

It should also be noted that no single expert can prepare all three data layers by him/herself and do the GIS analysis described in this chapter. Risk assessment is a multi-disciplinary endeavour that requires input from various fields. Flood modeling should be carried out by a hydrology expert, mapping the elements at risk requires GIS and Remote Sensing and/or surveying skills. Selection and assessment of the attributes and their values to assess vulnerability requires engineering, social and economic expertise. The steps in this analysis require a GIS expert with a basic understanding of what the experts from the other disciplines deliver and integrate all this information into a final risk assessment. 

Flood hazard

Most event-based flood models produce time series of maps with inundation depth and sometimes flow velocity and they usually produce aggregated maps such as maximum inundation depth and maximum flow velocity. Some models also produce other relevant output, such as e.g. maps that indicate the time a pixel was inundated for the first time. The exact outputs vary per flood model and the GIS expert should familiarize him/herself with what information the model expert can provide. 

Maximum inundation depth is the most frequently used parameter in hazard and risk assessment. however in example 2 and 4 also flow velocity is used. Ideally the flood (inundation) maps should be calculated for events with a given return period, e.g. the 5 year flood, the 20 year flood and the 50 year flood. Figure 2 gives those three maps for Dennery. Figure 3 shows the same maps but now classified. An example for Dennery is given in Figure 4.

 Figure 2: Three examples of maximum flood inundation depth maps (not classified) of Dennery, St. Lucia for the return periods 5, 20 and 5 years.


Figure 3: Three examples of maximum flood inundation depth maps (classified) of Dennery, St. Lucia for the return periods 5, 20 and 5 years.


Figure 4: The integration of the flood model results into a hazard map.



In October 2014 a survey was carried out in Dennery village in collaboration with a team of five members from the community. Some of them were Red Cross volunteers with experience in mapping and some of them had provided assistance during recent disasters in the community (e.g. in 2013). They received a short instruction before the field survey about the procedure of collecting and documenting the required data for exposure and vulnerability. In total, data was collected of 339 buildings. This Use Case uses that dataset but for the sake of example, it has been generalized and modified. The MSc thesis of Uwakwe (2015) is available here.


The identification of the elements at risk is an essential step for the characterization of physical vulnerability. Use Case 7.4, the Data Management Book chapter 5.2 and the Methodology Book chapter 5.2 give more information about elements at risk -  or exposure maps. However, identification is useful (see e.g. example 5) but it is often not sufficient. For risk assessment an understanding of the characteristics of the asset (element at risk) is essential. Elements at risk, for example buildings and population, have specific characteristics that define their physical vulnerability, e.g. for buildings these characteristics include wall material, building height from the ground, number of floors, etc. For population vulnerability information about the number of people in a building, the age distribution, number of people in the building during day or night time, etc. are necessary. It should also be noted that the characteristics differ for different hazards. For example, the characteristic of a building's shape is important for earthquakes while it is not so important for floods.

Often the main limitations for risk assessment at local scale is the lack of such detailed data regarding the characteristics of the elements at risk, or if such data exists, it may either be inadequate or may not be suitable for the level (scale) or purpose of the assessment. For example, Papathoma-Köhle et al (2007) noted in an assessment of physical vulnerability of elements at risk that the main limitation was data availability and costs. They suggested that data to characterize the elements at risk can be collected by aerial-photograph analysis and remote sensing, local authorities, questionnaires and field surveys. Also Kappes et al. (2012) indicated during an assessment of the physical vulnerability of multi-hazards (including flood) that lack of data was a problem. They suggested that complementary using alternative sources such as e.g. Google Street view (if available) or completion of questionnaires by people that reside in the hazardous areas, will considerably improve the vulnerability assessment.


Vulnerability information was collected during the survey through 39 interviews with people whose house was flooded during the December 2013 flood event -  see also Use Case 7.2. Several parameters were recorded of which four are used in this Use Case, more specifically in example 6:

  • The degree of damage to the buildings, graded from No damage (0) to Total Loss (1),
  • The inundation depth,
  • the building materials, and
  • the number of floors.

Uwakwe (2015) plotted the inundation depth vs the degree of damage for four types of buildings. The results are shown in Figure 5. Such a graph is called a stage-damage curve, or depth-damage curve, or flood-vulnerability curve. These data formed the basis of the vulnerability curves used in example 6, although slightly modified: Buildings of structural type 1 can be characterized by the building materials wood and brick; Buildings of structural type 2 can be characterized by the building materials concrete blocks and concrete; Buildings of structural type 3 can be characterized by a mix of building materials; There were only two records of buildings of structural type 4 (concrete blocks wall with ceramic tiles on the floor), so this category was not included in example 6.

Figure 5: Vulnerability curves for the structural types (source: Uwakwe 2015)

Type 1: Wood and brick; Type 2: Concrete blocks and concrete; Type 3: Mix of building materials; Type 4: Concrete blocks wall with ceramic tiles on the floor.

 Flood extent

The team members had experience with the December 2013 flood in Dennery and they drew a sketch map of the extent of the flood as they remembered. Figure 6a shows this sketch, Figure 6b shows the model results of the 5-year return period flood for comparison.