A predictive modelling tool for assessing climate, land use and hydrological change on reservoir quality.
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10348/4304 |
Resumo: | Reservoirs are fundamental for water and energy supply but vulnerable to impacts including climate change. This paper outlines the steps in the development of a model to predict how climate, land use and hydrological change could affect the physiochemical and ecological quality of reservoirs in Portugal’s Douro region. Climatic data will be downscaled for subsequent finer spatial scale models to develop scenarios and outputs. Field observations and satellite imagery analysis will create dynamic maps providing data on change in land use and vegetation cover, while Artificial Neural Networks will determine how climate, land use and vegetation cover change may influence catchment hydrology. Data from field surveys of biological indicators, greenhouse gas emissions plus additional research will be applied in the Stochastic Dynamic Methodology, a sequential modelling process based on statistical parameter estimation, developed to predict and model physiochemical and ecological changes in reservoirs. This interdisciplinary approach will provide vital modelling tools for end users essential for water resource management in Portugal and to comply with the EU Water Framework Directive. |
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A predictive modelling tool for assessing climate, land use and hydrological change on reservoir quality.PortugalClimate changeLande useHydrologyReservoirPredictive modellingReservoirs are fundamental for water and energy supply but vulnerable to impacts including climate change. This paper outlines the steps in the development of a model to predict how climate, land use and hydrological change could affect the physiochemical and ecological quality of reservoirs in Portugal’s Douro region. Climatic data will be downscaled for subsequent finer spatial scale models to develop scenarios and outputs. Field observations and satellite imagery analysis will create dynamic maps providing data on change in land use and vegetation cover, while Artificial Neural Networks will determine how climate, land use and vegetation cover change may influence catchment hydrology. Data from field surveys of biological indicators, greenhouse gas emissions plus additional research will be applied in the Stochastic Dynamic Methodology, a sequential modelling process based on statistical parameter estimation, developed to predict and model physiochemical and ecological changes in reservoirs. This interdisciplinary approach will provide vital modelling tools for end users essential for water resource management in Portugal and to comply with the EU Water Framework Directive.2015-03-17T14:20:33Z2012-01-01T00:00:00Z2012info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10348/4304eng0004-0894dodoi: 10.1111/j.1475-4762.2012.01114.xmetadata only accessinfo:eu-repo/semantics/openAccessHughes, Samantha JaneCabecinha, EdnaSantos, João Carlos Andrade dosAndrade, Cristina Maria MendesLopes, Domingos Manuel MendesTrindade, Henrique Manuel da FonsecaCabral, João AlexandreSantos, MárioLourenço, José Manuel MartinhoAranha, José Tadeu MarquesFernandes, Luís Filipe SanchesMorais, Maria ManuelaLeite, Maria Solange MendonçaOliveira, Paula Cristina Ribeiro Coutinho deCortes, Rui Manuel Vitorreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-02T12:37:13Zoai:repositorio.utad.pt:10348/4304Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:01:42.047204Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A predictive modelling tool for assessing climate, land use and hydrological change on reservoir quality. |
title |
A predictive modelling tool for assessing climate, land use and hydrological change on reservoir quality. |
spellingShingle |
A predictive modelling tool for assessing climate, land use and hydrological change on reservoir quality. Hughes, Samantha Jane Portugal Climate change Lande use Hydrology Reservoir Predictive modelling |
title_short |
A predictive modelling tool for assessing climate, land use and hydrological change on reservoir quality. |
title_full |
A predictive modelling tool for assessing climate, land use and hydrological change on reservoir quality. |
title_fullStr |
A predictive modelling tool for assessing climate, land use and hydrological change on reservoir quality. |
title_full_unstemmed |
A predictive modelling tool for assessing climate, land use and hydrological change on reservoir quality. |
title_sort |
A predictive modelling tool for assessing climate, land use and hydrological change on reservoir quality. |
author |
Hughes, Samantha Jane |
author_facet |
Hughes, Samantha Jane Cabecinha, Edna Santos, João Carlos Andrade dos Andrade, Cristina Maria Mendes Lopes, Domingos Manuel Mendes Trindade, Henrique Manuel da Fonseca Cabral, João Alexandre Santos, Mário Lourenço, José Manuel Martinho Aranha, José Tadeu Marques Fernandes, Luís Filipe Sanches Morais, Maria Manuela Leite, Maria Solange Mendonça Oliveira, Paula Cristina Ribeiro Coutinho de Cortes, Rui Manuel Vitor |
author_role |
author |
author2 |
Cabecinha, Edna Santos, João Carlos Andrade dos Andrade, Cristina Maria Mendes Lopes, Domingos Manuel Mendes Trindade, Henrique Manuel da Fonseca Cabral, João Alexandre Santos, Mário Lourenço, José Manuel Martinho Aranha, José Tadeu Marques Fernandes, Luís Filipe Sanches Morais, Maria Manuela Leite, Maria Solange Mendonça Oliveira, Paula Cristina Ribeiro Coutinho de Cortes, Rui Manuel Vitor |
author2_role |
author author author author author author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Hughes, Samantha Jane Cabecinha, Edna Santos, João Carlos Andrade dos Andrade, Cristina Maria Mendes Lopes, Domingos Manuel Mendes Trindade, Henrique Manuel da Fonseca Cabral, João Alexandre Santos, Mário Lourenço, José Manuel Martinho Aranha, José Tadeu Marques Fernandes, Luís Filipe Sanches Morais, Maria Manuela Leite, Maria Solange Mendonça Oliveira, Paula Cristina Ribeiro Coutinho de Cortes, Rui Manuel Vitor |
dc.subject.por.fl_str_mv |
Portugal Climate change Lande use Hydrology Reservoir Predictive modelling |
topic |
Portugal Climate change Lande use Hydrology Reservoir Predictive modelling |
description |
Reservoirs are fundamental for water and energy supply but vulnerable to impacts including climate change. This paper outlines the steps in the development of a model to predict how climate, land use and hydrological change could affect the physiochemical and ecological quality of reservoirs in Portugal’s Douro region. Climatic data will be downscaled for subsequent finer spatial scale models to develop scenarios and outputs. Field observations and satellite imagery analysis will create dynamic maps providing data on change in land use and vegetation cover, while Artificial Neural Networks will determine how climate, land use and vegetation cover change may influence catchment hydrology. Data from field surveys of biological indicators, greenhouse gas emissions plus additional research will be applied in the Stochastic Dynamic Methodology, a sequential modelling process based on statistical parameter estimation, developed to predict and model physiochemical and ecological changes in reservoirs. This interdisciplinary approach will provide vital modelling tools for end users essential for water resource management in Portugal and to comply with the EU Water Framework Directive. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-01-01T00:00:00Z 2012 2015-03-17T14:20:33Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10348/4304 |
url |
http://hdl.handle.net/10348/4304 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0004-0894 dodoi: 10.1111/j.1475-4762.2012.01114.x |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799137101737885696 |