Integration of local and scientific knowledge to support drought impact monitoring: some hints from an Italian case study

Raffaele Giordano (), Elisabetta Preziosi and Emanuele Romano

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2013, vol. 69, issue 1, 523-544

Abstract: According to the Hyogo Framework for Action, increasing resilience to drought requires the development of a people-centered monitoring and early warning system, or in other words, a system capable of providing useful and understandable information to the community at risk. To achieve this objective, it is crucial to negotiate a credible and legitimate knowledge system, which should include both expert and local knowledge. Although several benefits can be obtained, the integration of local and scientific knowledge to support drought monitoring is still far from being the standard in drought monitoring and early warning. This is due to many reasons, that is, the reciprocal skepticism of local communities and decision makers, and the limits in the capacity to understand and assess the complex web of drought impacts. This work describes a methodology based on the sequential implementation of Cognitive Mapping and Bayesian Belief Networks to collect, structure and analyze stakeholders’ perceptions of drought impacts. The methodology was applied to analyze drought impacts at Lake Trasimeno (central Italy). A set of drought indicators was developed based on stakeholders’ perceptions. A validation phase was carried out comparing the perceived indicators of drought and the physical indicators (i.e., Standard Precipitation Index and the level of the lake). Some preliminary conclusions were drawn concerning the reliability of local knowledge to support drought monitoring and early warning. Copyright Springer Science+Business Media Dordrecht 2013

Keywords: Drought risk management; Hyogo Framework for Action; Drought monitoring and early warning; Participatory monitoring; Cognitive map; Bayesian belief network (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s11069-013-0724-9

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