The decrease in flood risk for the road agency is calculated as the difference between EAD in the baseline scenario and EAD with the intervention (using Equations 1 and 2). Following interventions, the repair and rehabilitation cost (after floods) will be reduced, as damage will be lower or avoided in some cases.

With some of the interventions, a major intervention on the road surface is assumed to take place. As the condition of the surface improves, the road user cost is reduced independently from flood events. The benefits will be incurred by the road users daily and will depend on traffic. This benefit is calculated as traffic multiplied by the difference between RUC (aggregated over all OD pairs) between the baseline and intervention scenario, in the absence of floods. Similarly, because of a major intervention on the road surface the routine maintenance expenditure will be reduced giving additional annual benefits to the road agency (the maintenance savings are detailed in the annex and are obtained from RONET model).

Finally, a recent study by the World Bank finds a correlation between reduction of transport cost and increase of agriculture production in Mozambique (Iimi, forthcoming). The agriculture production is found to increase by 2.7% for a reduction of 10% in transport costs. This study is used to calculate the increase in agriculture production following a decrease in road user costs. In addition, we assume that traffic growth increases by 1 percentage points when agriculture production increases by 1%. There is a lot of uncertainty in this elasticity, so we used a range of elasticity from 0.5 to 1.5. The traffic increase is defined with the following equation and is based on the above-mentioned assumptions, where ���<sup>�</sup> is traffic volume in year _y_ , �� is national traffic increase, ��� is agriculture to traffic elasticity, ��� is transport cost to agriculture production elasticity ���� is baseline road user cost and ∆���<sup>�</sup> is reduction of road user cost by intervention i.

We detail below the benefits attributed to each of the interventions.

# **Upgrade of surface treatment**

When roads are paved, the road user cost is assumed to be 0.23 $/km (equivalent to an IRI of 4). When the paved road is close to an agriculture cluster, it is assumed that agriculture production increases by 2.7 points for each 10 point of decrease on road user cost and that the traffic will increase following equation 5 introduced above.

Disruptions are reduced as new drainage structures (culverts) have been put in place to accommodate the new paved road. For instance, an unpaved road was disrupted with 50cm of water on top of the road surface while the paved road can drain 80cm before the road is disrupted (as it is assumed that a new culvert can drain the rainfall from a 20-year return period event). Damages from floods on the infrastructure are considerably lower and so is the repair cost as it is assumed that the condition rate (used in equation 1) is set to 1.

# **Upgrade to gravel road**

Here it is assumed that damaged roads are rehabilitated to restore the initial design. Most of the roads in Zambezia and Nampula are in poor or bad condition due to deficient maintenance and periodic maintenance so this intervention would upgrade the earth road to a gravel one in good conditions. The road user cost is thus assumed to be 0.27$/km (equivalent to an IRI of 7). The surface and class type are not changed. Like the previous intervention, when the gravel road is close to an agriculture cluster it is assumed that agriculture production would increase using the 2.7/10 relation and thus traffic increases using an elasticity that ranges from 0.5 to 1.5.

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