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Previsão de secas na primavera em Portugal Continental com base em indicadores climáticos de larga escala

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Previsão de secas na primavera em Portugal Continental com base em indicadores climáticos de larga escala

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dc.contributor.author Santos, J.F es_ES
dc.contributor.author Portela, M.M. es_ES
dc.contributor.author Pulido-Calvo, I. es_ES
dc.coverage.spatial east=-8.224454000000037; north=39.39987199999999; name= Portugal
dc.date.accessioned 2017-01-20T12:07:56Z
dc.date.available 2017-01-20T12:07:56Z
dc.date.issued 2015-10-30
dc.identifier.issn 1134-2196
dc.identifier.uri http://hdl.handle.net/10251/77097
dc.description.abstract [EN] The success of a strategy of mitigation of the effects of the droughts requires the implementation of an effective monitoring and forecasting system, able to identify drought events and follow their spatiotemporal evolution. This article demonstrates the capability of the artificial neural networks in predicting the spring standardized precipitation index, SPI, for Portugal. The validation of the models used the hindcasting, which is a technique by which a given model is tested through its application to historical data followed by the comparison of the results thus achieved with the data. The SPI index was calculated at the timescale of six months and the climate indices used as external predictors in the hindcasting were the North Atlantic Oscillation and temperatures of the sea surface. The study showed the added value of the inclusion of previous predictors in the model. Maps of the probabilities of the drought occurrences which may be very important for integrated planning and management of water resources were also developed. es_ES
dc.description.abstract [PT] O sucesso de uma estratégia de mitigação dos efeitos da seca passa pela implementação de um sistema de monitorização e previsão eficaz, capaz de identificar os eventos de seca e de seguir a sua evolução espácio-temporal. Neste artigo demonstrase a eficiência de redes neuronais artificiais na previsão, para Portugal, do índice de precipitação padronizada, SPI, relativo à primavera. A validação dos modelos recorreu ao hindcasting, designando-se, por tal, a técnica através da qual um dado modelo é testado mediante a sua aplicação a períodos temporais históricos, com comparação dos resultados obtidos com as respectivas observações. O índice SPI foi calculado à escala temporal de 6 meses tendo o hindcast utilizado como indicadores climáticos a oscilação do Atlântico Norte e temperaturas da superfície do mar. O estudo evidenciou a mais valia da inclusão dos anteriores predictores externos no modelo de previsão. Elaboraram-se, ainda, mapas de probabilidade de ocorrência de seca os quais constituem importantes ferramentas no planeamento integrado e na gestão de recursos hídricos es_ES
dc.language Portugués es_ES
dc.publisher Universitat Politècnica de València
dc.relation.ispartof Ingeniería del Agua
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject SST es_ES
dc.subject NAO es_ES
dc.subject SPI es_ES
dc.subject Hindcasting es_ES
dc.subject Artificial neural networks es_ES
dc.title Previsão de secas na primavera em Portugal Continental com base em indicadores climáticos de larga escala es_ES
dc.title.alternative Spring drought forecasting in mainland Portugal based on large-scale climatic indices es_ES
dc.type Artículo es_ES
dc.date.updated 2017-01-20T11:45:06Z
dc.identifier.doi 10.4995/ia.2015.4109
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Santos, J.; Portela, M.; Pulido-Calvo, I. (2015). Previsão de secas na primavera em Portugal Continental com base em indicadores climáticos de larga escala. Ingeniería del Agua. 19(4):211-227. https://doi.org/10.4995/ia.2015.4109 es_ES
dc.description.accrualMethod SWORD es_ES
dc.relation.publisherversion https://doi.org/10.4995/ia.2015.4109 es_ES
dc.description.upvformatpinicio 211 es_ES
dc.description.upvformatpfin 227 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 19
dc.description.issue 4
dc.identifier.eissn 1886-4996
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