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A characterisation model to address the environmental impact of green water flows for water scarcity footprints.
Quinteiro, Paula; Rafael, Sandra; Villanueva-Rey, Pedro; Ridoutt, Bradley; Lopes, Myriam; Arroja, Luís; Dias, Ana Cláudia.
Afiliação
  • Quinteiro P; Centre for Environmental and Marine Studies (CESAM), Department of Environment and Planning, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal. Electronic address: p.sofia@ua.pt.
  • Rafael S; Centre for Environmental and Marine Studies (CESAM), Department of Environment and Planning, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal. Electronic address: sandra.rafel@ua.pt.
  • Villanueva-Rey P; Centre for Environmental and Marine Studies (CESAM), Department of Environment and Planning, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal; Department of Chemical Engineering, Institute of Technology, Universidade de Santiago de Compostela, 15782 Santiago de Compo
  • Ridoutt B; University of the Free State, Department of Agricultural Economics, Bloemfontein 9300, South Africa; Commonwealth Scientific and Industrial Research Organisation (CSIRO), Private Bag 10, Clayton South, Victoria 3169, Australia. Electronic address: Brad.Ridoutt@csiro.au.
  • Lopes M; Centre for Environmental and Marine Studies (CESAM), Department of Environment and Planning, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal. Electronic address: myr@ua.pt.
  • Arroja L; Centre for Environmental and Marine Studies (CESAM), Department of Environment and Planning, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal. Electronic address: arroja@ua.pt.
  • Dias AC; Centre for Environmental and Marine Studies (CESAM), Department of Environment and Planning, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal. Electronic address: acdias@ua.pt.
Sci Total Environ ; 626: 1210-1218, 2018 Jun 01.
Article em En | MEDLINE | ID: mdl-29898528
ABSTRACT
The development of methods to assess the potential environmental impact of green water consumption in life cycle assessment has lagged behind those for blue water use, which are now routinely applied in industrial and policy-related studies. This represents a critical gap in the assessment of land-based production systems and the ability to inform policy related to the bio-economy. Combining satellite remote sensing and meteorological data sets, this study develops two new sets of spatially-differentiated and globally applicable characterisation factors (CFs) to assess the environmental impact of green water flows in LCA. One set of CFs addresses the impact of shifts in water vapour flow by evapotranspiration on blue water availability (CFWS) and the other set of CFs addresses moisture recycling within a basin (CFWA). Furthermore, as an additional and optional step, these two indicators are combined into an aggregated green water scarcity indicator, representing the global variability of green water scarcity. The values obtained for CFWA show that there are significant changes in green water flows that were returned to the atmosphere in Alaska (covered by open shrublands) and in some central regions of China (covered by grasslands and barren or sparsely vegetated land), where precipitation levels are lower than 10 mm/yr. The results obtained for CFWS indicate that severe perturbations in surface blue water production occur, particularly in central regions of China (covered by grasslands), the southeast of Australia (covered by evergreen broadleaf forest) and in some central regions of the USA (covered by grassland and evergreen needleleaf forest). The application of the green water scarcity CFs enables the evaluation of the potential environmental impact due to green water consumption by agricultural and forestry products, informing both technical and non-technical audiences and decision-makers for the purpose of strategic planning of land use and to identify green water protection measures.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Total Environ Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Total Environ Ano de publicação: 2018 Tipo de documento: Article