A Bayesian networks approach for the integrated assessment of climate change impacts on water quality

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dc.contributor.advisor Critto, Andrea <1971> it_IT
dc.contributor.author Sperotto, Anna <1987> it_IT
dc.date.accessioned 2017-09-11 it_IT
dc.date.accessioned 2019-04-06T05:48:32Z
dc.date.available 2019-04-06T05:48:32Z
dc.date.issued 2018-02-01 it_IT
dc.identifier.uri http://hdl.handle.net/10579/14078
dc.description.abstract Climate change is triggering new water management challenges affecting regional and global water quantity and quality. Despite potential impacts of climate change on water availability have been widely studied in the last decades, the implication for concomitant changes in water quality have been just poorly explored. Variations in temperature and precipitation patterns, are likely to have profound effects on those hydrological processes (e.g. runoff, river flow, water retention time, evapotranspiration) that regulate the mobilization of pollutants from land to water bodies however, such signals, can be masked by those of concurring local stressors (i.e. land use, point and diffuse pollution sources).Breaking down the relative role played by each of these pressures and predicting their combined impacts is necessary to mainstream the implementation of well-targeted adaptation measures supporting sectorial policies and legislations. Accordingly, the adoption of a multi-stressor perspective to water quality assessment is required to drawn realistic base lines and reasonable targets steering future water resource management strategies. A data driven risk framework based on Bayesian Networks was implemented in the Zero river basin (Northern Italy) to characterize the interlacing between climate change and land use practices and assess their cascading impacts on water quality status (i.e. nutrients loadings). Bayesian Networks were used as meta-modelling tool for structuring and combining the information available by existing monitored data, hydrological models, climate change projections producing alternative risk scenarios to communicate the probability of changes in the amount nutrients (i.e. NO3-, NH4+, PO43-) delivered from the basin under different climate change projections (i.e. RCP 4.5 and 8.5). Specifically, an Ensemble of temperature and precipitation projections downscaled from available Global and Regional Climate models (i.e. GCMs-RCMs) were directly used to inform the Bayesian Network in order to account for uncertainties across climate change scenarios and river basin responses and to determine the level of confidence of projected water quality alterations between baseline and future climate regimes. Bayesian Network outputs help in tracking future trends of water quality and in supporting the prioritization of stressors and pollution sources. Overall, developed risk scenarios, can be used as baselines against which test and evaluate existing management and adaptation measures and targets for water quality.Simulated scenarios show that seasonal changes in precipitation and temperature are likely to modify both the hydrology and nutrients loadings of the Zero River and that diffuse pollution sources play a key role in determining the amount of nutrients loaded while point source have only a marginal effect. Both NH4 and PO4 loadings, in fact, are mainly influenced by changes in the climatic and hydrological variables while NO3 loadings are strongly affected by agronomic practices and land use. Results have been evaluated through a cross comparison with existing observed data and hydrological models’ simulations (i.e. SWAT) available for the case study providing a reasonable agreement.. it_IT
dc.language.iso en it_IT
dc.publisher Università Ca' Foscari Venezia it_IT
dc.rights © Anna Sperotto, 2018 it_IT
dc.title A Bayesian networks approach for the integrated assessment of climate change impacts on water quality it_IT
dc.title.alternative it_IT
dc.type Doctoral Thesis it_IT
dc.degree.name Scienza e gestione dei cambiamenti climatici it_IT
dc.degree.level Dottorato di ricerca it_IT
dc.degree.grantor Dipartimento di Economia it_IT
dc.description.academicyear 30 CICLO + PROLUNGAMENTI E SOSPENSIONI 29 CICLO it_IT
dc.description.cycle 30 it_IT
dc.degree.coordinator Barbante, Carlo it_IT
dc.location.shelfmark D001841 it_IT
dc.location Venezia, Archivio Università Ca' Foscari, Tesi Dottorato it_IT
dc.rights.accessrights openAccess it_IT
dc.thesis.matricno 810770 it_IT
dc.format.pagenumber 121 p. it_IT
dc.description.note it_IT
dc.degree.discipline it_IT
dc.contributor.co-advisor Torresan, Silvia <1980> it_IT
dc.provenance.upload Anna Sperotto (810770@stud.unive.it) it_IT
dc.provenance.plagiarycheck Andrea Critto (critto@unive.it) it_IT

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