Resilience from the ground up: how are local resilience perceptions and global frameworks aligned?

Numerous resilience measurement frameworks for climate programmes have emerged over the past decade to operationalise the concept and aggregate results within and between programmes. Proxies of resilience, including subjective measures using perception data, have been proposed to measure resilience, but there is limited evidence on their validity and use for policy and practice. This article draws on research on the Decentralising Climate Funds project of the Building Resilience and Adaptation to Climate Extremes and Disasters programme, which supports communities in Mali and Senegal to improve climate resilience through locally controlled adaptation funds. It explores attributes of resilience from this bottom‐up perspective to assess its predictors and alignment with food security, as a proxy of well‐being. We find different patterns when comparing resilience and the well‐being proxy, illustrating that the interplay between the two is still unclear. Results also point to the importance of contextualising resilience, raising implications for aggregating results.     

Ensemble predictions and perceptions of risk,uncertainty, and error in flood forecasting

Under the auspices of the World Meteorological Organization, there are a number of international initiatives to promote the development and use of so-called ensemble prediction systems (EPS) for flood forecasting. The campaign to apply these meteorological techniques to flood forecasting raises important questions about how the probabilistic information these systems provide can be used for what in operational terms is typically a binary decision of whether or not to issue a flood warning. To explore these issues, we report on the results of a series of focus group discussions conducted with operational flood forecasters from across Europe on behalf of the European Flood Alert System. Working in small groups to simulate operational conditions, forecasters engaged in a series of carefully designed forecasting exercises using various different combinations of actual data from real events. Focus group data was supplemented by a follow-up questionnaire survey exploring how flood forecasters understand risk, uncertainty, and error. Results suggest that flood forecasters may not instinctively use ensemble predictions in the way that promoters of EPS perhaps think they should. The paper concludes by exploring the implications of these divergent ‘epistemic cultures’ for efforts to apply ensemble prediction techniques developed in the context of weather forecasting to the rather different one of flood forecasting.     

Ensemble predictions and perceptions of risk, uncertainty, and error in flood forecasting

Under the auspices of the World Meteorological Organization, there are a number of international initiatives to promote the development and use of so-called ensemble prediction systems (EPS) for flood forecasting. The campaign to apply these meteorological techniques to flood forecasting raises important questions about how the probabilistic information these systems provide can be used for what in operational terms is typically a binary decision of whether or not to issue a flood warning. To explore these issues, we report on the results of a series of focus group discussions conducted with operational flood forecasters from across Europe on behalf of the European Flood Alert System. Working in small groups to simulate operational conditions, forecasters engaged in a series of carefully designed forecasting exercises using various different combinations of actual data from real events. Focus group data was supplemented by a follow-up questionnaire survey exploring how flood forecasters understand risk, uncertainty, and error. Results suggest that flood forecasters may not instinctively use ensemble predictions in the way that promoters of EPS perhaps think they should. The paper concludes by exploring the implications of these divergent ‘epistemic cultures’ for efforts to apply ensemble prediction techniques developed in the context of weather forecasting to the rather different one of flood forecasting.