Climate Crisis: How Big Data Can Improve Disaster Response and Preparedness

Big data can aid in getting urgent information to those who need it during floods and other events. Photo: Pok Rie
Big data can aid in getting urgent information to those who need it during floods and other events. Photo: Pok Rie

By Madhavi Pundit, Lena Nur, Belinda Hewitt

During times of disaster and crisis, fast and detailed information is needed. Big data solutions have the potential to save lives and economic assets if the right frameworks are put in place. This will be increasingly important as climate change takes its toll.

As climate change accelerates, disasters triggered by natural hazards are becoming more frequent and severe. When they strike, government agencies need reliable, readily understandable, and rapidly available information on impacts so that they can make informed decisions on how to respond. 

This information is critical for targeting response and early recovery measures and to start mobilizing domestic and external resources for longer-term recovery. 

Speed is of the essence.  Delayed response to tropical cyclones, floods, earthquakes, droughts, epidemics, and other events can prolong suffering and result in significant costs to households, governments, and the economy at large.

Early and proactive disaster response, in turn, can help households and businesses protect their assets and avoid loss of income and its knock-on effects. It also has the potential to reduce the total price tag for response, recovery, and rehabilitation by prioritizing efforts effectively.

Traditional methods for obtaining impact information after a disaster are often costly and may take a long time to carry out. Post-disaster needs assessments (PDNA), where governments and other stakeholders collate impact information and define response, recovery and reconstruction needs are a common prerequisite for accessing external assistance from donors and multilateral development banks. 

PDNAs and longer-term recovery and reconstruction planning often depend upon field data typically collected via ground surveys and stakeholder interviews which are time consuming and require significant resources, including personnel, time, and funding.  

Many governments and organizations struggle to allocate resources effectively in the immediate aftermath of a disaster when information is scarce. Gathering data is further complicated if access to affected areas is difficult, dangerous, or restricted, either because of the disaster or due to other factors such as ongoing conflict. The information gap reduces the effectiveness of recovery and results in lost opportunities to ‘build back better'.

Forecasting and analysis tools that use big data, including remote sensing, together with more ‘traditional’ data like censuses, provide rapid, relevant, and granular information on the likely socio-economic impacts of disasters. The information is also relatively low cost, and produced in near real time or even before a disaster to enable a timely and well-informed response and more resilient recovery. Importantly, these tools have the potential to speed up existing practices for generating and translating post-disaster damage and loss information into prioritised needs, and therefore enhance post-disaster aid appeals, PDNAs, and recovery planning with their unique strengths.

Big data tools can make communities in Asia and the Pacific more resilient to climate change impacts.

To effectively utilize big data in enhancing disaster management and response, governments and other institutions can take the following actions: 

  • Secure continued data access and standardize protocols: This involves ensuring ongoing access to relevant data between providers and users. It also encompasses the standardization of data structures and protocols to ensure consistency and interoperability across different systems and platforms. This is crucial for maintaining a seamless flow of information in disaster and emergency contexts.
  • Enhance national government processing capacity and technical skills:  Disaster management agencies need to strengthen their capacities to handle large datasets efficiently. This includes enhancing the technical skills of personnel through training and development programs. Support from regional entities in the form of technical backstopping can enhance expertise and resources, fostering a collaborative approach to disaster management.
  • Integrate new data and tools within established disaster risk management processes: Big data tools, such as impact-based forecasting and nowcasting, should be seamlessly integrated into existing disaster risk management frameworks. This integration ensures that new technologies complement and enhance traditional methods.
  • Allocate finance to develop big data and related tools, and actions based on information produced:  Allocating adequate financial resources for the acquisition and maintenance of big data tools is essential.

This includes budgeting not just for the tools themselves but also for the actions and decisions that are taken based on the insights provided by these tools. Effective budgeting and financing strategies will ensure that the potential of big data in disaster management is fully realized and effectively employed to reduce the impact of disasters.

These actions can help to protect lives and livelihoods before, during, and after disasters, when time is critical.  Big data tools can make communities in Asia and the Pacific more resilient to climate change impacts.