cultivated crops contribution to GDP at provincial level. The same holds for employment. It is thus assumed here that there are no significant differences in productivity or yield among different provinces. For rice, a slightly different methodology has been applied. Export value and jobs are disaggregated based on provincial share of area used for rice production. To arrive at provincial rice output (in ton), provincial rice area (in hectare) and provincial rice yield (ton/ha) are used. Then, jobs or monetary value at risk is calculated multiplying the provincial level estimates with the provincial level exposure for the different return periods. Detailed province-level results for each return period and type of flooding are summarized in the technical annex to the main report (De Vries Robbé et al. 2020).

## 5.2. Economic impacts of flooding in the aquaculture sector

The economic impact of flooding on aquaculture production is estimated based on the exposure calculated in section 4. For each coastal province in the Red River and the Mekong Deltas, the analysis of section 4 gives the share of aquaculture production affected by any flooding of a certain return period. Multiplying the affected share with the provincial aquaculture output in tons, aquaculture contribution to GDP in $ or number of aquaculture jobs at the province level gives an estimate of the economic impact of each flood event in each province. To arrive a provincial disaggregation of export, GDP and jobs, the national values are distributed among the provinces using provincial aquaculture production numbers. Here assumption is made of that factors as yield and productivity are uniform across provinces. Put differently, a province creating X percent of national aquaculture production is assumed to produce X percent of national aquaculture exports. Detailed province-level results for each return period and type of flooding are summarized in the technical annex to the main report (De Vries Robbé et al. 2020).

## 5.3. Economic impacts of flooding in the tourism sector

For tourism, economic estimations are conducted for flooding and coastal erosion. The share of hotels exposed to each of the two hazards in each coastal province is sourced from the exposure analysis in section 4. For flooding, this consists of the share of exposed hotels by 25cm flooding for each flood return period. For erosion, this is the share of coastal hotels affected by strong or moderate coastal erosion. The methodology then proceeds in two steps. First, each province’s contribution to tourism GDP is calculated by dividing national tourism GDP by the share of hotels in each province. Analogously, the number of jobs in the tourism industry is distributed among provinces. In the second step, the share of hazard-affected hotels is applied to the amount of GDP or jobs affected for each province, each hazard, and each hazard return period. This results in a province-level estimate of affected GDP and jobs, once for coastal erosion and once for each flood return period. Detailed province-level results for each return period and type of flooding are summarized in the technical annex to the main report (De Vries Robbé et al., 2020).

# 6. Discussion and conclusion

In conducting a quantitative analysis, this paper relies on the quality of the underlying hazard and sectoral data. The spatial hazard data used to model coastal flooding and typhoon wind exposure are extracted from global hazard models. Such global models require global datasets as input and incur significant computational complexity. Consequently, they have a coarse spatial resolution, like a grid cell size of about 900x900m for coastal flooding in Vietnam. In reality flood depth is not uniform within each 900x900m block of land, but heterogeneous due to the elevation profile within that block. Consequently, 30