their use results in a loss of information when applied at a local scale. The distorting effect of this is somewhat reduced by using spatial buffers around asset location points. Conceptually, these buffers could be imagined as reducing the asset location point resolution and thus counteracting some of the distorting effects of low-resolution hazard data. In addition, the underestimation of flood extent for extreme coastal flooding in tropical regions, together with the absence of wave setup in the coastal data, will probably result in an underestimation of the effect of flooding, especially in regions where the coast is relatively steep. In future, the development of localized natural hazard models at the national scale could add significant value to risk assessments, especially in the case of coastal flooding.

As the flood maps used here are modelled without information on flood protection, this paper uses an unconventional approach to incorporate flood protection infrastructure into the exposure estimates (section 4.1). The flood exposure offset determined for each province, every return period, and every flood type requires some limiting assumptions – for example, that protection infrastructure protects all assets within a province uniformly. This assumption might be violated for assets like industrial zones, which might be more likely to be situated in places with more advanced protection infrastructure. For other assets such as rice fields, this assumption seems more likely to hold. Still, to avoid severely overestimating exposure when not accounting for protection systems in place, this approach seems reasonable for the scale of the analysis conducted. While it prohibits exposure estimates at the subprovincial level, it proves useful for estimates at the provincial and national level. Future research aimed at incorporating protection infrastructure into large-scale flood modelling could help make such workarounds redundant.

As exposure is derived from the combination of hazard and asset information, the results produced hinge on the quality of the asset data to the same degree. The geolocated datasets of hotels and schools are sourced from volunteer contributions, and do not represent all hotels and schools in Vietnam (sections 3.6 and 3.5). It is possible that the nature of crowd-contribution leads to biases in the representation of subgroups of each asset class. For example, as crowdsourcing is unpaid and requires an internet connection, contributions made could overrepresent more easily accessible areas, and lead to the underrepresentation of installations from poorer or otherwise less accessible villages. This is especially true for the results for schools, where the provincial distribution does not match the distribution from the General Statistics Office of Vietnam, and should be interpreted with great care. Still, in data-sparse environments, openly available data can provide second-best options for approximating hazard exposure. Despite their imperfection, such estimates can be valuable in disproving the absence of risk which may be suggested by the absence of information. Results relying on crowd-sourced data thus do not represent hard facts, but best estimates forming the basis for discussion.

In other sectors, asset and socioeconomic data is more reliable, and this leads to greater certainty in the results regarding their exposure. In particular, the analysis uses datasets leveraging new developments in the processing of remote sensing data. These datasets provide highly granular estimates of the locations of specific assets like aquaculture ponds, data which would be prohibitively expensive to collect by other means. Due to the cheapness of computing power and the ease of automated spatial analysis, information about exposure can be created at higher resolutions than ever before. As methodologies to extract specific asset locations from various types of remote sensing data become more established and the barriers to implement these algorithms become lower, the translation of such methodologies to different settings can be expected to produce insights into distributions of natural hazard risk in new regions.

Overall, this paper shows that the exposure of Vietnam’s people and their economy is significant, but that this exposure varies greatly. It varies across space as some provinces are more exposed than others. It 31