<mark>This does not include other considerations such as production losses due to supply chain disruptions, or welfare implications of lost access to critical services and supplies.</mark> To calculate the baseline travel delay cost, we first estimated the travel time for each commuter trip (8,866 total) under five flood scenarios using the walking, the drive and the public transit networks based on travel mode information reported in the JICA survey. Next, we calculated travel delay for each flood scenario by subtracting the travel time under dry condition from the estimations. This result was then combined with the personal monthly income information provided in the JICA survey. For each OD trip, we multiplied the delay in travel time due to flooding by the income per unit of time assuming that each worker works eight hours a day, five days a week, and on average 22 days a month. The corresponding output was then doubled to account for return trips from work. To adjust for the difference between sample size and survey population, the cost was multiplied by respective weighting factors for each trip. This same process was repeated for five flood return periods namely: 10-year, 50-year, 100-year, 500-year and 1000-year and the economic cost for all 8,866 trips were later aggregated to the TAZ level. Table 7 shows the number of commuters’ trips affected by five flood scenarios under different delay thresholds. Figure 17 illustrates maps of the averaged economic cost due to travel delay for each TAZ under all flood scenarios. We observe that areas towards the center of Kinshasa city experience relatively higher cost than peripheral suburbs in the south-east and northeast with the exception of the smaller TAZs situated in the heart of the city. It should be noted that the averaged cost aggregation at the TAZ level is more representative in the central regions of the city. Due to the uneven spatial distribution of sampling locations, the peripheral TAZs only contain a few sampling points. For example, the Kinsielele region towards the south of the city shows up with relatively high travel delay costs under all flood scenarios yet there are only 11 sampling points in this particular TAZ.

Figure 18 shows two sets of grouped boxplots of estimated travel time delay in minutes and corresponding economic loss for different levels of personal income under five flood scenarios. In general, floods with longer return periods (more extreme) on average induce longer travel delays and more economic cost compared with those of shorter return periods. When it comes to the variation or uncertainty of flood impacts, the 100-year and 500-year floods are relatively higher than floods of other return periods. Specifically, the 100-year flood’s impact on commuters’ travel delays has the highest variation/uncertainty and the 500-year flood is the highest in terms of economic cost variation/uncertainty. When we examine these two types of impact: travel delay and economic cost by different income groups, it is clear that commuters with medium income levels (monthly salary from $100 to $1,000) experience relatively higher travel delays than low income (monthly salary below $100) commuters and high income (monthly salary greater than $3,000) commuters. It should be noted that the lower-income groups have more extreme estimates (over one hour) in travel delays which suggest the existence of highly disadvantaged commuter groups who are heavily impacted by flood disruptions on road networks.

Table 7: Number of commuter trips affected (8866 total) by delay thresholds.

|**Flood return period**|**>5 min delay**|**>10 min delay**|**>30 min delay**|**>1 hour delay**|**>2 hour delay**|**Number of**<br>**failed trips**|
|---|---|---|---|---|---|---|
|**10-year flood**|7520|6491|4261|2418|342|679|
|**50-year flood**|7007|6047|4085|2810|1049|1217|
|**100-year flood**|6779|5842|4032|3027|1821|1453|
|**500-year flood**|5940|5047|3408|2590|1822|2329|
|**1000-year flood**|5344|4475|2878|2108|1477|2924|

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