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

Author

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  • Anna Sperotto

    (Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (Fondazione CMCC), c/o via Augusto Imperatore 16, 73100 Lecce, Italy
    Department of Environmental Sciences, Informatics and Statistics, University Ca’ Foscari Venice, 30123 Venezia, Italy)

  • Josè Luis Molina

    (High Polytechnic School of Engineering, University of Salamanca, Av. de los Hornos Caleros, 50, 05003 Ávila, Spain)

  • Silvia Torresan

    (Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (Fondazione CMCC), c/o via Augusto Imperatore 16, 73100 Lecce, Italy)

  • Andrea Critto

    (Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (Fondazione CMCC), c/o via Augusto Imperatore 16, 73100 Lecce, Italy
    Department of Environmental Sciences, Informatics and Statistics, University Ca’ Foscari Venice, 30123 Venezia, Italy)

  • Manuel Pulido-Velazquez

    (Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València, 46022 València, Spain)

  • Antonio Marcomini

    (Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (Fondazione CMCC), c/o via Augusto Imperatore 16, 73100 Lecce, Italy
    Department of Environmental Sciences, Informatics and Statistics, University Ca’ Foscari Venice, 30123 Venezia, Italy)

Abstract

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 (NO 3 , NH 4 , PO 4 ) 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.

Suggested Citation

  • Anna Sperotto & Josè Luis Molina & Silvia Torresan & Andrea Critto & Manuel Pulido-Velazquez & Antonio Marcomini, 2019. "Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks," Sustainability, MDPI, vol. 11(17), pages 1-34, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4764-:d:262781
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    References listed on IDEAS

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    1. Marcot, Bruce G., 2012. "Metrics for evaluating performance and uncertainty of Bayesian network models," Ecological Modelling, Elsevier, vol. 230(C), pages 50-62.
    2. Yiannis Panagopoulos & Christos Makropoulos & Maria Mimikou, 2011. "Diffuse Surface Water Pollution: Driving Factors for Different Geoclimatic Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(14), pages 3635-3660, November.
    3. Uusitalo, Laura, 2007. "Advantages and challenges of Bayesian networks in environmental modelling," Ecological Modelling, Elsevier, vol. 203(3), pages 312-318.
    4. Hui Xu & Daniel G. Brown & Allison L. Steiner, 2018. "Sensitivity to climate change of land use and management patterns optimized for efficient mitigation of nutrient pollution," Climatic Change, Springer, vol. 147(3), pages 647-662, April.
    5. Daniel Wallach & Linda O. Mearns & Alex C. Ruane & Reimund P. Rötter & Senthold Asseng, 2016. "Lessons from climate modeling on the design and use of ensembles for crop modeling," Climatic Change, Springer, vol. 139(3), pages 551-564, December.
    6. Kelli L. Larson & Dave D. White & Patricia Gober & Amber Wutich, 2015. "Decision-Making under Uncertainty for Water Sustainability and Urban Climate Change Adaptation," Sustainability, MDPI, vol. 7(11), pages 1-24, November.
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    2. Meng-Leong How & Yong Jiet Chan & Sin-Mei Cheah, 2020. "Predictive Insights for Improving the Resilience of Global Food Security Using Artificial Intelligence," Sustainability, MDPI, vol. 12(15), pages 1-14, August.

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