Economic damage calculations

The economic damage assessment was carried out for the three types of damages, building, road and crop damage, separately since the input data and calculation methods for each type differ. In a final step, the total estimated economic damage is calculated by combining the damages for each of these land use categories.

2.3.2.1 Building damage

Building replacement values were calculated for each building separately. Using the point damages as such in the visualization would lead to a visual underestimation of the total damage, since the damaged points would seem very small in comparison to the entire study area. In order to generate a better visual result and facilitate the interpretation of the resulting building damage map, the building point features were aggregated per land use polygon. All replacement values of all buildings in one polygon were added up to one building replacement value per land use polygon, creating maximum damage zones.

In a second step, the maximum building damage map is combined with the flood map to create a map of the estimated building damage, corresponding to the 2001 flood event.

Since most houses are somewhat elevated from ground level, the methodology adopts an average doorstep level of 0.5 m that the flood water has to overcome before a building will be inundated (Deckers et al., 2009; ODPEM, 2013a; Vanneuville et al., 2003a).

Therefore, 0.5 m is deducted from the water depths for all buildings. The depth-damage functions, shown in Table 5, for building-structure and building-content originate from a model developed for Japan (Dutta et al., 2003). These functions were selected on the basis of several considerations and, in particular, (i) the structure of buildings in Japan is similar to that of Jamaican buildings, as most Japanese and Jamaican buildings are constructed with solid concrete or wooden walls and no insulating material, (ii) there is a distinction between wooden and concrete buildings, a distinction that has also been made in the database for buildings in Annotto Bay, (iii) damage factors are expressed in percentages instead of absolute values, as it is the case for many other studies; this is essential to implement the methodology in this case study as well as in other areas. The depth-damage functions were as part of a flood loss estimation model that combines a physically based distributed hydrologic model and a distributed flood loss estimation model. The depth-damage functions designed in this study are based on damage data from past floods, gathered by the Japanese Ministry of Construction since 1954 (Dutta et al., 2003).

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THE URBAN CASE STUDY: ANNOTTO BAY, JAMAICA

Table 5 Flood damage factors (%) for wooden and concrete structures and their content, based on Dutta et al. (2003).

Since a maximum building damage value was calculated per damage zone, the two depth-damage functions for wooden and concrete structures cannot be used separately; therefore, an average value of the two functions was used. The value of the estimated damage to structures and content with the respective damage factors are defined as direct damage. In addition to direct damage, indirect damage, which involves for example clean-up costs, was also calculated for buildings. This value is expressed as a percentage of the direct damage, varying from 1% to 15%, dependent on the damage degree of the structure (Vanneuville et al., 2002). When the flood level is relatively low, but the water has entered the building, the total damage will be low as well, but the indirect clean-up costs will be substantial compared to the overall damage.

However, when the damages are higher, due to high flood levels, the clean-up cost will not increase at the same rate. Therefore, the indirect damage percentage is calculated using following empirical equation:

(5) With

48

direct damage. In addition to direct damage, indirect damage, which involves for example clean-up costs, was also calculated for buildings. This value is expressed as a percentage of the direct damage, varying from 1% to 15%, dependent on the damage degree of the structure (Vanneuville et al., 2002). When the flood level is relatively low, but the water has entered the building, the total damage will be low as well, but the indirect clean-up costs will be substantial compared to the overall damage. However, when the damages are higher, due to high flood levels, the clean-up cost will not increase at the same rate.

Therefore, the indirect damage percentage is calculated using following empirical equation:

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝐼𝐼𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝐼𝐼 (%) = 1 + (1 −^{2∗ 𝛼𝛼𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠}₃^{+ 𝛼𝛼𝑐𝑐𝑐𝑐𝑐𝑐}) × (15 − 1) (5)

With 𝛼𝛼

as the damage factor for structure and 𝛼𝛼

as the damage factor for content. The total estimated damage to buildings is the sum of direct damage and indirect damage.

Every building must be accessible from a road. This assumption was applied in this assessment by creating two buffers around the road network, at 25 meters and at 60 meters. Samples in the study area showed that 90 percent of all buildings are located in the 25 meters buffer zone and 99 percent in the 60 meters buffer zone. These buffers are added to the calculations as extra polygons. Since the resulting damage maps show the damage per m², large polygons will cause the damage to be spread evenly over a large surface and visually seem low. The extra polygons, that contain the largest part of the buildings, and thus the largest building damages, will aid in a better allocation of the damage, as this same damage will be spread over a smaller polygon. The visual result of this adaptation is illustrated in Figure 12.

FFiigguurree 1122.. Visual comparison between building damage calculations without buffers (left) and with buffers (right).

The spatial distribution of building damage is more accurate and high risk areas are distinctly indicated, but there is no difference in the total damage cost calculations.

as the damage factor for structure and

48

direct damage. In addition to direct damage, indirect damage, which involves for example clean-up costs, was also calculated for buildings. This value is expressed as a percentage of the direct damage, varying from 1% to 15%, dependent on the damage degree of the structure (Vanneuville et al., 2002). When the flood level is relatively low, but the water has entered the building, the total damage will be low as well, but the indirect clean-up costs will be substantial compared to the overall damage. However, when the damages are higher, due to high flood levels, the clean-up cost will not increase at the same rate.

Therefore, the indirect damage percentage is calculated using following empirical equation:

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝐼𝐼𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝐼𝐼 (%) = 1 + (1 −^{2∗ 𝛼𝛼𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠}₃^{+ 𝛼𝛼𝑐𝑐𝑐𝑐𝑐𝑐}) × (15 − 1) (5)

With 𝛼𝛼

as the damage factor for structure and 𝛼𝛼

as the damage factor for content. The total estimated damage to buildings is the sum of direct damage and indirect damage.

Every building must be accessible from a road. This assumption was applied in this assessment by creating two buffers around the road network, at 25 meters and at 60 meters. Samples in the study area showed that 90 percent of all buildings are located in the 25 meters buffer zone and 99 percent in the 60 meters buffer zone. These buffers are added to the calculations as extra polygons. Since the resulting damage maps show the damage per m², large polygons will cause the damage to be spread evenly over a large surface and visually seem low. The extra polygons, that contain the largest part of the buildings, and thus the largest building damages, will aid in a better allocation of the damage, as this same damage will be spread over a smaller polygon. The visual result of this adaptation is illustrated in Figure 12.

FFiigguurree 1122.. Visual comparison between building damage calculations without buffers (left) and with buffers (right).

The spatial distribution of building damage is more accurate and high risk areas are distinctly indicated, but there is no difference in the total damage cost calculations.

as the damage factor for content. The total estimated damage to buildings is the sum of direct damage and indirect damage.

Every building must be accessible from a road. This assumption was applied in this assessment by creating two buffers around the road network, at 25 meters and at 60 meters. Samples in the study area showed that 90 percent of all buildings are located in the 25 meters buffer zone and 99 percent in the 60 meters buffer zone. These buffers are added to the calculations as extra polygons. Since the resulting damage maps show the damage per m², large polygons will cause the damage to be spread evenly over a large surface and visually seem low. The extra polygons, that contain the largest part of the buildings, and thus the largest building damages, will aid in a better allocation of the damage, as this same damage will be spread over a smaller polygon. The visual result of this adaptation is illustrated in Figure 12.

direct damage. In addition to direct damage, indirect damage, which involves for example clean-up costs, was also calculated for buildings. This value is expressed as a percentage of the direct damage, varying from 1% to 15%, dependent on the damage degree of the structure (Vanneuville et al., 2002). When the flood level is relatively low, but the water has entered the building, the total damage will be low as well, but the indirect clean-up costs will be substantial compared to the overall damage. However, when the damages are higher, due to high flood levels, the clean-up cost will not increase at the same rate.

Therefore, the indirect damage percentage is calculated using following empirical equation:

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝐼𝐼𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝐼𝐼 (%) = 1 + (1 −^{2∗ 𝛼𝛼𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠}₃^{+ 𝛼𝛼𝑐𝑐𝑐𝑐𝑐𝑐}) × (15 − 1) (5)

With 𝛼𝛼

as the damage factor for structure and 𝛼𝛼

as the damage factor for content. The total estimated damage to buildings is the sum of direct damage and indirect damage.

Every building must be accessible from a road. This assumption was applied in this assessment by creating two buffers around the road network, at 25 meters and at 60 meters. Samples in the study area showed that 90 percent of all buildings are located in the 25 meters buffer zone and 99 percent in the 60 meters buffer zone. These buffers are added to the calculations as extra polygons. Since the resulting damage maps show the damage per m², large polygons will cause the damage to be spread evenly over a large surface and visually seem low. The extra polygons, that contain the largest part of the buildings, and thus the largest building damages, will aid in a better allocation of the damage, as this same damage will be spread over a smaller polygon. The visual result of this adaptation is illustrated in Figure 12.

FFiigguurree 1122.. Visual comparison between building damage calculations without buffers (left) and with buffers (right).

The spatial distribution of building damage is more accurate and high risk areas are

distinctly indicated, but there is no difference in the total damage cost calculations.

Figure 12 Visual comparison between building damage calculations without buffers (left) and with buffers (right).

The spatial distribution of building damage is more accurate and high risk areas are distinctly indicated, but there is no difference in the total damage cost calculations.

Especially large land use polygons without any damage except building damage, for example in the wetland area, benefit immensely from these buffers.

2.3.2.2 Crop damage

The depth-damage functions for crops in Table 6 originate from the Japanese model developed by Dutta et al. (2003) as well. In this model, there is a detailed distinction between types of crops, similar to the ones that are most cultivated in Jamaica. In contrary to the depth-damage functions for buildings by Dutta et al. (2003) that only take water depth into consideration, the depth-damage functions for crops take into account both water depth and flood duration. In this case, the flood lasted for two days (ODPEM, 2013a). Since the exact crops cultivated in Annotto Bay are unknown, an average damage factor was calculated based on the damage values as described by Dutta et al. (2003). Indirect damage, which primarily involves production losses, was set at 10% of the direct damage (Vanneuville et al., 2002). These two values are summed up to obtain the total estimated damage for crops.

Table 6 Flood damage factors for crops for a two-day flood, based on Dutta et al. (2003).

WATER DEPTH

(meters) BEANS CHINESE

CABBAGE DRY CROPS MELON

0.00 0 0 0 0

0.30 30 58 30 30

0.61 30 58 30 30

0.91 40 66 43 39

WATER DEPTH

(meters) PADDY VEGETABLE WITH

ROOT SWEET POTATO GREEN LEAVE VEGETABLES

0.00 0 0 0 0

0.30 24 43 26 24

0.61 24 43 26 24

0.91 38 74 40 44

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THE URBAN CASE STUDY: ANNOTTO BAY, JAMAICA

An adequate depth-damage function for banana plants was not found in literature.

Therefore, a new function, based on the characteristics of the banana plant, was drafted.

These plants can only survive water saturation conditions up to 48 hours because of the fragile roots (Rajamannan, 2004). Consequently, the depth-damage function is a linear function that reaches 100% damage after 48 hours of inundation. The water depth was not taken into account as this parameter was not important. The indirect damage is 10%

of the direct damage.

2.3.2.3 Road damage

A road dataset of the area was available for this research, giving a class and corresponding width to each road (ODPEM, 2013a). Although this information allowed a more precise damage calculation, a comparison with satellite imagery showed big disparities between the available dataset and the reality. Many unpaved roads were missing and the simplification of the data, due to its larger scale, caused the location of other roads to differ from reality. Additionally, damage to roads showed to be signifi-cantly smaller than building damage. Therefore, the priority was given to adding all unpaved roads, losing the class information, in order to enhance the accuracy of the building damage spread.

Most roads and bridges in Jamaica are not elevated. Consequently, they suffer considerable damage during a flood. The depth-damage function for roads is the same as in LATIS because roads are constructed in the same manner in both regions and will thus suffer the same proportion of damage. The damage factor for roads is expressed by the following function (Vanneuville et al., 2003c):

(6) where

22..33..22..33 RRooaadd ddaam maaggee

A road dataset of the area was available for this research, giving a class and corresponding width to each road (ODPEM, 2013a). Although this information allowed a more precise damage calculation, a comparison with satellite imagery showed big disparities between the available dataset and the reality. Many unpaved roads were missing and the simplification of the data, due to its larger scale, caused the location of other roads to differ from reality. Additionally, damage to roads showed to be significantly smaller than building damage. Therefore, the priority was given to adding all unpaved roads, losing the class information, in order to enhance the accuracy of the building damage spread.

Most roads and bridges in Jamaica are not elevated. Consequently, they suffer considerable damage during a flood. The depth-damage function for roads is the same as in LATIS because roads are constructed in the same manner in both regions and will thus suffer the same proportion of damage. The damage factor 𝑓𝑓 for roads is expressed by the following function (Vanneuville et al., 2003c):

𝑓𝑓 = 𝑚𝑚𝑚𝑚𝑚𝑚 (0.28 × 𝑑𝑑; 0.18 × 𝑑𝑑 + 0.1 ; 1) (6)

where 𝑑𝑑 represents the water depth in meters.

22..33..33 SSoocciiaall ddaam maaggee ccaallccuullaattiioonnss

The methodology used to calculate the number of people killed is very similar to the one used to calculate the economic damage. Small statistical sectors were drawn based on similar characteristics such as building density and land use; inhabitants were assumed to be spread homogeneously across the number of houses. As such, the number of inhabitants per sector was calculated based on the number of houses per statistical sector (ODPEM, 2011) and the number of people per household in 2001, which was an average of 3 for St. Mary (WRA, 2002). The spread of the inhabitants per statistical sector based on the number of houses per sector was statistically tested and has proven to be significant with a significance level of 5%. The number of casualties is expressed by Equation (2) from Chapter 1 (Vrisou van Eck et al., 1999). Since there was no available rise velocity information for the flood event of 2001, the drown factor 𝑓𝑓

based on the rise velocity was set at 100%, which corresponds to a rise velocity higher than 3 m per hour. This is the safest presumption, but could lead to an overestimation of the number of casualties.

The drown factor based on flood depth is expressed by the following equation:

𝑓𝑓𝑑𝑑= 𝑒𝑒𝑒𝑒𝑒𝑒 (1.16 × 𝑑𝑑 − 7.3) (7)

represents the water depth in meters.