¶ Purpose
The purpose of the GO risk module is to visualise curated data on disaster risks in order to inform decision-making and enhance situational awareness concerning ongoing crises.
The evidence and analytical tools are public goods and use the best-available open-source, open-access data from IFRC, National Societies as well as partners from governments, academia, the UN, the private sector and other international organisations.
¶ What it does
¶ Imminent events
The first component of the risk module, which is visible on regional and country pages, presents information about forecast or recently detected hazardous events (e.g., hazard type, expected or observed locations).
For more information about the forecasted impacts, such as the number of people, schools, hospitals, and homes affected, click on the icon for any one of the hazards:
Click on the “i” tooltip to find about more of the source of the impact forecasts. The pop-up box will include a link to detailed information about the input data and means of analysis used to produce these estimates:
¶ Seasonal risk
The second thing the risk module does is visualize information about the magnitude of risk and exposure per country, per month (or per return period) with respect to certain hazards. These include the risk of people becoming displaced or food insecure, for example. This information is visualized several ways on the platform:
On regional pages a map dynamically updates itself based on which hazard, risk, and month – or months – you select:
Regional and country pages also present the same data as a table displaying the numerical (or categorical) values for each hazard and risk. As with the estimated impacts of imminent events, you can click on the “i” tooltip for information about the risk estimates:
Country pages also present the same data as a vertical bar chart that shows the magnitude of the risks per hazard for all 12 months of the year:
You can also click to forecasts and historical values, where data is available:
Wherever data is available, a table summarises the observed/modeled impacts expected for events with specific return periods:
On country pages, a graph with data on past disasters plots events based on the number of people that were affected (along the y-axis) and which month they occurred (along the x-axis):
If a National Society has an approved EAP, or if early or anticipatory actions have been implemented within that country, you will find information about these on country pages as well:
¶ How to use it – some common use cases
¶ Use case 1: Taking action to mitigate the impacts of a forecasted (or recent) hazard
In this scenario, one would see that a hazardous event has been forecasted or just occurred on the risk watch tab of the regional or country page. The first thing one would do is click on the hazard icon to see what is known – or what has been estimated – about it:
Above, for example, we see that for this particular flood in Viet Nam, more than 95,000 people are exposed and may be affected; of these, approximately 6,700 people are considered to be vulnerable due to their socioeconomic status.
The next step is to see how these potential impacts compare with past events and to see what the nature of the response was in the previous instance(s) using the historical timeline from the risk watch tab of the page of the country in question:
In terms of the number of people affected, we see that this flood disaster may be similar to three previous IFRC operations:
- the October 2020 floods which affected 110,000 people and for which there was a DREF request approved for just over CHF 2 million;
- the October 2010 floods, which affected 120,000 people and for which there was a DREF request approved for just over CHF 1 million; and
- the December 1998 floods, which affected 100,000 people and for which there was a DREF request approved for approximately CHF 4.2 million.
¶ Use case 2: Planning anticipatory actions
If one is wondering which countries are most at risk of disasters associated with hydrometeorological hazards within a given region, one can evaluate the total risk levels on the regional page, selecting annual risk. This shows which countries have the highest risk for droughts, floods, and storms – regardless of which months those events occur within any given country:
For the example above, in relation to floods, the INFORM risk data suggests that risk is highest in several countries across the Sahel, as well as Somalia and Madagascar; meanwhile, probabilistic flood models indicate that the country with the highest number of people exposed to flooding on an annual basis is Nigeria followed by Sudan, the DRC and Tanzania. Next, one would check if an EAP had already been approved by looking on that National Society’s country page. If not, that would be a sign for colleagues from the regional office to reach out to the country delegation to see if the National Society in question is interested in developing an EAP.
Below, we see that in Sudan, that the greatest flood risk is in August based on an analysis of INFORM monthly data and population exposure data. If it takes six months, on average, to develop and approve an EAP, this means we would need to reach out in January or February at the latest to have the instrument in place when the worst flooding usually occurs in Sudan.
Below, we see that according to the IDMC disaster displacement risk model, a 1-in-20-year flood would displace approximately 455,000 people, a figure that aligns with the data on past events we have from the Sudanese Red Crescent from which we see that only one flood affected that many people in the last 25 years.
If the EAP were designed to help the Sudanese Red Crescent respond to more frequently occurring floods, we would know that the impact forecast trigger should be fewer than 455,000 people affected or displaced. Instead, we see that there were five events that affected at least 100,000 people meaning this is the range of impact we would consider if intending to address impacts associated with 1-in-5-year flood events. Of course, this would be just one piece of information needed to develop the a flood trigger for forecast-based action.