CIDAS

Problem:

The IFRC aim to allocate resources to take proactive measures to mitigate the effects of relatively high-frequency, low-intensity natural hazards. Although these events are categorised as low-impact, they significantly impact the poorest communities in developing countries and they continue to receive inadequate support. In the future, the IFRC intends to disburse CHF 100 million from its Disaster Relief Fund. To allocate funding, they require more insight into the impact of the retrospective events we are analysing.

Our Solution:

To assess when an event(forecasted or occurred) reaches the impact threshold, we need to produce impact exceedance curves on observational data for specific types of events. We are using publicly available datasets mainly the national disaster loss database(DesInventar) and EM-DAT to produce exceedance curves for multiple impacts for as many National Societies as possible.

Expected Outputs:

• Impact exceedance curves and tables.
• A fully documented model for automatically updating it based on the availability of new observational data.

Achievement and impact:

Our project aims to support the IFRC in allocating funding more effectively to neglected countries in need, by producing impact exceedance curves based on observational data for specific types of natural disasters. This will allow the IFRC to anticipate the impact of future events and take preventive measures, as well as use the model for risk analysis. By making the model open-source, all national societies can access and use it to improve their disaster response and reduce the impact of natural disasters on vulnerable communities.

We are proud to announce, that we will be meeting several government agencies, including teams from United Nations Disaster Risk Reductions (UNDRR) and EM-DAT to present our solution. Furthermore, our results will also be published on the IFRC GO platform alongside our GitHub Repositories with open-source access for people to build upon.