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Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks

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Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks

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dc.contributor.author Sperotto, A. es_ES
dc.contributor.author Molina, J.L. es_ES
dc.contributor.author Torresan, S. es_ES
dc.contributor.author Critto, A. es_ES
dc.contributor.author Pulido-Velazquez, M. es_ES
dc.contributor.author Marcomini, A. es_ES
dc.date.accessioned 2020-05-22T03:01:58Z
dc.date.available 2020-05-22T03:01:58Z
dc.date.issued 2019-08-31 es_ES
dc.identifier.uri http://hdl.handle.net/10251/144074
dc.description.abstract [EN] With increasing evidence of climate change affecting the quality of water resources, there is the need to assess the potential impacts of future climate change scenarios on water systems to ensure their long-term sustainability. The study assesses the uncertainty in the hydrological responses of the Zero river basin (northern Italy) generated by the adoption of an ensemble of climate projections from 10 di erent combinations of a global climate model (GCM)¿regional climate model (RCM) under two emission scenarios (representative concentration pathways (RCPs) 4.5 and 8.5). Bayesian networks (BNs) are used to analyze the projected changes in nutrient loadings (NO3, NH4, PO4) in mid- (2041¿2070) and long-term (2071¿2100) periods with respect to the baseline (1983¿2012). BN outputs show good confidence that, across considered scenarios and periods, nutrient loadings will increase, especially during autumn and winter seasons. Most models agree in projecting a high probability of an increase in nutrient loadings with respect to current conditions. In summer and spring, instead, the large variability between di erent GCM¿RCM results makes it impossible to identify a univocal direction of change. Results suggest that adaptive water resource planning should be based on multi-model ensemble approaches as they are particularly useful for narrowing the spectrum of plausible impacts and uncertainties on water resources. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sustainability es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Water quality es_ES
dc.subject Climate change es_ES
dc.subject Bayesian networks es_ES
dc.subject Uncertainty es_ES
dc.subject Multi-models es_ES
dc.subject.classification INGENIERIA HIDRAULICA es_ES
dc.title Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/su11174764 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient es_ES
dc.description.bibliographicCitation Sperotto, A.; Molina, J.; Torresan, S.; Critto, A.; Pulido-Velazquez, M.; Marcomini, A. (2019). Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks. Sustainability. 11(17):1-34. https://doi.org/10.3390/su11174764 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/su11174764 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 34 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 11 es_ES
dc.description.issue 17 es_ES
dc.identifier.eissn 2071-1050 es_ES
dc.relation.pasarela S\411939 es_ES
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dc.subject.ods 06.- Garantizar la disponibilidad y la gestión sostenible del agua y el saneamiento para todos es_ES


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