http://www.cnr.it/ontology/cnr/individuo/prodotto/ID272930

Integration of local and scientific knowledge to support drought impact monitoring: some hints from an Italian case study (Articolo in rivista)

Type

Prodotto della ricerca (Classe)
Articolo in rivista (Classe)

Label

Integration of local and scientific knowledge to support drought impact monitoring: some hints from an Italian case study (Articolo in rivista) (literal)

Anno

2013-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1007/s11069-013-0724-9 (literal)

Alternative label

Giordano R., Preziosi E., Romano E. (2013)
Integration of local and scientific knowledge to support drought impact monitoring: some hints from an Italian case study
in Natural hazards (Dordr.)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Giordano R., Preziosi E., Romano E. (literal)

Pagina inizio

523 (literal)

Pagina fine

544 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

69 (literal)

Rivista

Natural hazards (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

CNR, Water Research Institute (literal)

Titolo

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

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. (literal)

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