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To accurately identify the most vulnerable areas to floods, physical (e.g., building material) and social (e.g., education, health, income of households) housing stock information is required. However, in developing countries, this information is often unreliable, unavailable or inaccessible, and manual data collection is time-consuming. This can lead to difficulties for humanitarians or policymakers in implementing appropriate disaster risk reduction and response interventions. Therefore, there is a need for the development of alternative approaches to data collection and analysis. An alternative approach to on-site vulnerability assessment is to extract physical vulnerability characteristics, such as land use type or rooftop material, from satellite or Unmanned Aerial Vehicle (UAV) imagery. However, other social or physical vulnerability information on the household level can often not be extracted from only the remote sensing data. This research develops an approach for integrating multiple data sources into a Geographic Information System to improve the completeness of data on different vulnerability indicators. This approach is applied on the housing stock of the Karonga district in Malawi. An Object-Based Image Analysis on UAV imagery is combined with a machine learning analysis of Mapillary data to enable remote identification of both rooftop ànd wall material. Depth-damage curves were created to describe the impact on the housing stock for different categories of physical vulnerability (such as building material) and levels of inundation. Moreover, local survey data is used for the creation of a social vulnerability index. Combined, the datasets represent the spatial distribution of housing stock vulnerability for multiple flood scenarios. This approach is useful in situations where proactive risk analyses must be carried out or where local-scale interventions, such as building strengthening- or flood awareness projects, have to be implemented. Finally, we give recommendations for scaling the methodology to areas where only lower resolution data is available.

With a global paradigm shift from post-disaster response aid to anticipatory action, the question on how anticipatory action relates to long-term climate adaptation and often government-led actions towards permanent disaster prevention becomes more relevant. With rising disaster risk, a framework that decision-makers can use to select between preventive and preparedness risk reduction efforts would be most useful. A model originally developed to compare permanent interventions to forecast-based action for floods was applied to wind-induced building damage due to tropical cyclones, focusing on a case study from the Philippines. We made use of a typhoon forecasting model based on the ensemble forecast from EMCWF, and modeled the wind footprint to estimate the wind speed in the case study area. A threshold was defined, similar to how it is done in actual operations by the Philippine Red Cross. If the forecasted typhoon exceeds a pre-set threshold in terms of wind speed, action to strengthen light-weight wooden houses with a Shelter Strengthening Kit (SSK) is taken. SSKs temporarily make these houses more resistant to withstand extreme winds, thereby reducing the impacts. This short term action is compared to a scenario in which lightweight wooden houses are permanently upgraded. Results give actors in humanitarian response, anticipatory action as well as permanent disaster prevention insight into which variables affect this balance. and help policymakers to allocate their scarce budgets in a cost-effective way. The framework, although developed for the Philippines, can also be replicated in other cyclone-prone countries.