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Article

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

1
Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (Fondazione CMCC), c/o via Augusto Imperatore 16, 73100 Lecce, Italy
2
Department of Environmental Sciences, Informatics and Statistics, University Ca’ Foscari Venice, 30123 Venezia, Italy
3
High Polytechnic School of Engineering, University of Salamanca, Av. de los Hornos Caleros, 50, 05003 Ávila, Spain
4
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València, 46022 València, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(17), 4764; https://doi.org/10.3390/su11174764
Received: 1 August 2019 / Revised: 22 August 2019 / Accepted: 23 August 2019 / Published: 31 August 2019
(This article belongs to the Special Issue Sustainability in the Development of Water Systems Management)
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 different 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 different 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. View Full-Text
Keywords: water quality; climate change; Bayesian networks; uncertainty; multi-models water quality; climate change; Bayesian networks; uncertainty; multi-models
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MDPI and ACS Style

Sperotto, A.; Molina, J.L.; Torresan, S.; Critto, A.; Pulido-Velazquez, M.; Marcomini, A. Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks. Sustainability 2019, 11, 4764. https://doi.org/10.3390/su11174764

AMA Style

Sperotto A, Molina JL, Torresan S, Critto A, Pulido-Velazquez M, Marcomini A. Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks. Sustainability. 2019; 11(17):4764. https://doi.org/10.3390/su11174764

Chicago/Turabian Style

Sperotto, Anna, Josè L. Molina, Silvia Torresan, Andrea Critto, Manuel Pulido-Velazquez, and Antonio Marcomini. 2019. "Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks" Sustainability 11, no. 17: 4764. https://doi.org/10.3390/su11174764

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