|BT|9%-15%|4.3|10%|0.4|30|0.4||US$350,000|US$151,505||
|---|---|---|---|---|---|---|---|---|---|---|
|GR|>15%|1.8|10%|0.2|3|1.8||US$50,000|US$88,275||
|GR|>15%|1.8|15%|0.3|10|0.8||US$50,000|US$39,724||
|GR|>15%|1.8|20%|0.4|30|0.4||US$50,000|US$17,655||
|BT|>15%|2.9|2.5%|0.1|3|0.7|5.1|US$350,000|US$252,574|US$903,375|
|BT|>15%|2.9|10%|0.3|10|0.9||US$350,000|US$303,089||
|BT|>15%|2.9|20%|0.6|30|0.6||US$350,000|US$202,059||
|Total||41.5||||9.6|9.6||US$1,481,694|US$1,481,694|

# Annex 2. Methodology for Calculating Affected Population

The East Road runs across wards Fauabu (#4) and Nafinua (#15). The population in these two wards was estimated using distribution data from WorldPop open source data (updated in year 2015). The total population was estimated at 14,700. Using Census data for 2015, the estimated population in wards #4 and #5 would be 13,767, based on an average growth rate between 1999 and 2009. Given that the difference between the two estimation was not significant (as seen in the table below), the WorldPop open source data were adopted as the baseline population layer for the ArcGIS models.

|Ward #|Ward Name|Census data<br>(2015)|WorldPop Estimates<br>(2015)|
|---|---|---|---|
|4|Fauabu|9,207|9,858|
|15|Nafinua|4,560|4,842|
||Total Population|13,767|14,700|

With the WorldPop data, the fine-grain level population distribution was established. With a 3-km buffer zone, a total population of 10,648 could potentially be affected potentially with respect to market access, whereas within a 10-km buffer zone, a total population of 31,867 could potentially be affected with respect to access to hospitals.

Using ArcGIS, the East Road was divided into 5,856 segments based on the GIS information provided by the expert (self-collected data using a smartphone were compared with government data and since consistency was found, the former were used as the data input). The Haversine formula was used to determine the length of each segment connecting two points, which was then utilized to calculate the corresponding gradient of the segment. Each segment on average was about 5-10 meters in length. Each segment was then assigned with a numbered ID from east to west. A Python-automated program randomly selected a number of road segments (along the East Road) to be affected by a specified rain event and road upgrading combination. Under each rainfall scenario, each road design's gradient threshold would result in different damage profiles.

By using the random sampling selection method and identifying the nearest segment towards the Dala end of the road that would be hypothetically affected, the maximum affected population within the road buffer zones during road disruption was calculated. This calculation was based on the sum of people within the buffer zone to the east of the west-most affected road segment, minus the sum of people within a 3km/10km (based on the functionality being considered) distance of the west-most affected road segment. The latter was subtracted from the number of affected people because we assume that people within a 3km/10km distance of the west-most disrupted road segment can simply walk across and access the services required. The program was run 100 times for each combination, and the affected population was estimated based on an average of all 100 runs of the analysis.

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