The apocalypse is near.
Since 1970, the number of annual disasters worldwide have more than quadrupled.[i] In 2015 alone, natural disasters affected nearly 100 million people, leaving 22,773 dead, and resulting in $66.5bn of economic damage. This number is only projected to rise.[ii]
A costly bottleneck in disaster relief
Technological advances in radars, satellites, and machine-learning have improved our ability to predict catastrophe. Yet, disaster relief efforts remain predominately reactionary – particularly when it comes to raising the funds necessary to mobilize operations. According to UN figures, more than five time as much funding is spent on disaster response than disaster risk reduction.[iv]
Consider funding operations at the International Red Cross, a global humanitarian organization and leading disaster responder. The Red Cross operates with roughly 97 million volunteers, members, and staff worldwide. Its national subsidiary, The American Red Cross, responds to more than 700,000 US disasters on an annual basis alone.[v]
The Red Cross conducts some variation the following operations when disaster strikes:
- Assess damage
- Determine tasks required to relieve damage
- Triage tasks
- Unlock funding based on determined plan
- Mobilize (predominately) volunteer task forces[vi]
In developed nations, where funds are readily available, this process typically flows quickly. However, fundraising faces unpredictable timing in developing nations dependent on foreign aid. The result is a bottleneck that delays relief. An example of this catastrophic bottleneck was seen in 2010, when a flood occurred on the Mono River in Togo, West Africa and it took 34 days for international funding to reach the Red Cross in Togo.[vii]
Forecast-based financing promises to save time, money, and lives
Enter Forecast-based financing (FbF) – identified as one of the five Red Cross innovations of 2017.[viii] FbF leverages advancements in forecasting algorithms to anticipate and deploy the funds needed to trigger preventative measures before disaster strikes.
FbF operates by establishing a risk vs. cost threshold. Each threshold level is associated with a standard operating procedure agreed upon by humanitarian responders, meteorological services, and communities. Thus, forecasts with greater likelihood of disaster trigger more expensive operating procedures and vice versa.[ix]
The Red Cross pilots forecast-based-financing
In the near-term, The Red Cross has identified flood risk as a primary focus for FbF, piloting efforts in Peru, Bangladesh, and West Africa.[x] Flood risk was selected as the focus for several reasons:
- Existing self-learning algorithm: The Red Cross is coupling FbF with the roll-out of their existing FUNES flood risk prediction technology.[xi]
- Relationships with International Hydropower Association (IHA): Hydropower dams collect a wealth of data that can serve as a reliable input to FUNES.[xii]
- Closing a communication gap: Hydropower plants can often predict floods, yet they face communication gaps between operators and affected communities. The Red Cross aims to overcome that gap with FbF.[xiii]
In September, 2016, one of these early pilots paid-off. Using FUNES, the Red Cross anticipated a dangerous rise in the Nangbeto river dam and triggered funds to deploy operating procedures across 30 downstream communities. By the time the dam threatened to overflow, the Red Cross had successfully distributed cholera prevention hygiene kits, waterproof shelters, live radio spots, and other evacuation supplies. [xiv]
The Red Cross will continue monitoring its early pilots while simultaneously improving its predictive technology, such as FUNES, in order to further roll-out FbF around the globe. Ultimately, the Red Cross aims to extend FbF to broader disaster relief efforts, such as drought and fire.[xv]
In order to gain widespread adoption of FbF, The Red Cross will need to develop and refine two key factors:
- Quality data collection: the success of FbF relies on effective predictive modeling. Currently, data collection is often aggregated across volunteer sources using SMS. While crowdsourcing is cheap, it may lack accuracy at a mass scale. Increased investment in data collection techniques or partnerships with reliable data sources (i.e. IHA) will be essential to fine-tuning self-learning algorithms.
- Threshold alignment: Mapping the appropriate operating procedures to the right funding and risk thresholds is key. This will require actively evaluating the successes and failures of FbF in its early implementation and adjusting future models accordingly. In order to do so, communication and collaboration between communities, humanitarian leaders, and experts will be essential on an ongoing basis.
The Red Cross faces a critical junction: increased pressure to deliver global disaster relief yet increased technological opportunities to help address such demands. Is FbF the best digital investment to make in order to address bottlenecks in the disaster relief process? What additional efforts must The Red Cross consider in order to convince its global partners to support FbF on a mass scale?
[i] “Weather-related Disasters are Increasing,” The Economist, August 29, 2017, https://www.economist.com/blogs/graphicdetail/2017/08/daily-chart-19, accessed November 12, 2017.
[ii] “2015 Disasters in Numbers,” The United Nations Office for Disaster Risk Reduction, January 25, 2016, http://www.unisdr.org/files/47804_2015disastertrendsinfographic.pdf, accessed November 13, 2017.
[iii] “Weather-related Disasters are Increasing,” The Economist.
[iv] Jane Lubchenco, Jack Hayes, “New Technology Allows Better Extreme Weather Forecasts,” Scientific American, May 1, 2012, https://www.scientificamerican.com/article/a-better-eye-on-the-storm/, accessed November 12, 2017.
[vi]Juan Bazo, “Implementing Forecast-based Financing Mechanism in Peru,” Columbia University, https://iri.columbia.edu/wp-content/uploads/2015/11/RED_CROSS_-ELNI%C3%91OCONFERENCE_BAZO.pdf, accessed November 11, 2017.
[vii] Joshua Hill, “Red Cross Develops Innovative Mechanism to Predict & Prepare for Flood Risks,” Clean Technica, March 28, 2017, https://cleantechnica.com/2017/03/28/red-cross-develops-innovative-mechanism-predict-prepare-flood-risks/, accessed November 11, 2017.
[viii] “How to smartly utilize the window between forecast and hazard,” IFRC, February 1, 2017, https://media.ifrc.org/innovation/2017/02/01/forecast-based-financing-an-amazing-initiative-of-the-rcrc-climate-centre/, accessed November 11, 2017.
[xi] Hill, “Red Cross Develops Innovative Mechanism to Predict & Prepare for Flood Risks.”
[xv] Jaime Catalina, “Forecast-based Financing,” Understand Risk, https://understandrisk.org/forecast-based-financing/, Accessed November 11, 2017.