Regional EW4All MSF: Risk Knowledge for Early Warning, Early Action

The Early Warnings for All (EW4All) Initiative builds on existing interventions in countries, acknowledging that that most countries have been working on early warning systems for many years. This includes important work to enhance disaster risk knowledge to inform decisions in the short and long-term. Disaster risk knowledge encompasses mainly systematically collecting data and undertaking risk assessments. The ultimate aim is to ensure that hazards and vulnerabilities are well known and that the patterns and trends in these factors are identified so that they can be addressed.

Different approaches and technologies have importantly contributed to enhancing risk knowledge and links between risk assessments, decision-making and disaster prevention investments have been reinforced in Asia Pacific in the last years. However, despite this important progress, shortcomings related to data quantity and quality and capacities remain.

The role of science is critical to connect research and practices across different hazards, including hydrological, geological and biological hazards. Such multi-disciplinary and multi-hazard approaches allow for better understanding of cascading hazards and potential impacts of concurrent hazard events. In general, progress has been made in identifying, monitoring and modelling hazards, assessing vulnerabilities and exposure through increasingly comprehensive and advanced risk assessment tools. However, significant work remains to be done to tackle the limited understanding of systemic risk, including compounding and cascading risk and potential impacts.  

While the quality and availability of data on hazard exposure and of hazard models have significantly improved, more work is needed to define and apply methodologies to better integrate vulnerabilities, including socio-economic vulnerabilities, into risk assessments and collect, analyse and use disaggregated data on sex, age, disability and income.

Technological advances have improved the availability of data, the quality and diversity of sources of risk information. Remote sensing and geographic information systems have increasingly improved and are of great benefit for DRR and climate change adaptation. Big data innovations, including the use of large data sets from mobile phone tracking to satellite platforms, to delineate patterns, trends and linkages of the complex disaster risks are an important step forward in understanding disaster risk knowledge. Nevertheless, issues regarding fragmentation and limitations of data and challenges in terms of access to technology and capacity pose still important challenges. Historical disaggregated data is often missing, while at the same time a considerable number of databases, knowledge hubs, portals and platforms exist which requires further work on optimizing data sharing and data governance in the region. 

In this context, the private sector, which plays an important role in investments and thus in increasing or reducing disaster risk, also has considerable relevant data. Therefore, there is a need for significantly increased collaboration between the public and private sectors in sharing and analysing data and producing risk analyses. 

Also, continuous work on building capacity for better understanding of risk and producing risk analyses is needed, including enhancing capacities at the local governments and communities to collect, validate and consolidate risk information using disability inclusive and gender-responsive risk assessment tools.

This session aims at tacking stock of current approaches to enhance disaster risk knowledge for early warning, early action, including considerations of systemic, compounding and cascading risk and potential impacts. It will also offer the opportunity to showcase experiences on the use of technologies to enhance risk knowledge for early warning, early action, the involvement of private sector and on how to apply disaster risk knowledge for more inclusive and gender responsive early warning systems.