Effective weather-related disaster warnings highlight periods of increased risk, whether due to enhanced hazard likelihood (e.g. the approach of a storm), high levels of exposure (e.g. crowds gathering in a hazardous location for a festival) or high vulnerability (e.g. a health hazard during a flu outbreak). They depend on accurate monitoring and prediction of the weather and its related hazards, such as flood, landslide, excess heat or cold, wildfire and road icing, together with detailed knowledge of the exposure and vulnerability of communities in the affected area.
Early warnings typically involve a chain of organisations, with different objectives and responsibilities, and with different areas of expertise expressed in different technical language, communicating with each other to enable a final warning to be issued to the public. Not only are the expertise and technical language likely to be different, but each expert may choose to substitute other sources of information, including personal experience and indigenous knowledge, in preference to the “official” information stream. Such differences can make successful communication between these bodies as much of a challenge as is communication with the end user. Thus, the warning process may be represented as a chain of peaks of expertise, separated by communication valleys between the different languages and cultures of expertise, each of which may be bridged, more or less effectively, by procedures, information formats, training etc.
This paper uses the economic concept of a value chain to explore the extended warning production chain. The authors argue that by focussing on the chain as a whole, and on its connectivity represented by the bridges over the “valleys of death”, practitioners can contribute to the development and implementation of more effective warning systems that will build resilience to weather-related hazards.
This paper is a contribution to the 2019 edition of the Global Assessment Report on Disaster Risk Reduction (GAR 2019).
To cite this paper:
Golding, B. et al. A value chain approach to optimising early warning systems. Contributing Paper to GAR 2019