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

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 . 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.Sustainability 2019, 11, 4764 2 of 34 stemming from climate change make the assessment of climate change impacts on water resources particularly challenging [8].Uncertainty plays a prominent role in climate change science and climate change impact science, with hydrology and water resources research in particular [8][9][10][11]. According to Parker et al. [12] and Hawkins et al. [13], it can be attributed to a number of reasons including (i) scenario uncertainty, arising from our limited understanding about the path of greenhouse gasses emissions and socio-economic development; (ii) internal climate variability, due to the inherent variability of the climate system components, processes, and their interaction; (iii) model uncertainty, caused by the different formulations used to represent climatic processes in climate and impact models.A proper understanding of the type, sources, and effects of uncertainty is needed to achieve the goals of reliability and sustainability in water system management and planning under changing conditions [14,15]. Uncertainty quantification is vital to facilitate a risk-based approach to decision-making, where the range of possible futures is considered [16,17], and costs and benefits of adaptation are estimated accordingly. For this reason, uncertainties should be communicated as an inevitable component of each climate impact assessment study in a form which is also understandable by a non-scientific community to avoid misjudged information and to prevent overconfidence in impact projections [18].A promising way to evaluate and deal with uncertainty is represented by ensemble modelin...

Artificial Intelligence-Enhanced Decision Support for Informing Global Sustainable Development: A Human-Centric AI-Thinking Approach

“…BN can also be used to predict the so-called rare and unexpected worst-case "black swan" scenarios [39], and for failure analysis in systems [40]. In particular, BN excels in the counterfactual simulations of the conditions and their outcomes when there is uncertainty [41]. In the current paper, BN will be used to predict the best-case scenario, and also the worst-case black swan scenario of the uncertain conditions that could potentially adversely affect the EPI.…”

Source Apportionment of Nutrient Loads to a Mediterranean River and Potential Mitigation Measures

“…Indeed, a reduction in nutrients, emerging pollutants and metals could be achieved by adopting more advanced WWTPs or alternatives such as phytoremediation systems [54]. Mitigation measures to reduce the nutrient loads delivered to the surface waters are urgent because nutrient loadings were found particularly sensitive to climate change [55], especially considering that it is expected for the future an extension of the dry season and an exacerbation of the extreme low flow conditions [18]. Implementation of the measures tested in the present study would require investment from both the public and private sectors.…”

Artificial Intelligence-Enhanced Predictive Insights for Advancing Financial Inclusion: A Human-Centric AI-Thinking Approach

“…Researchers have also utilized BN to quantify the concept of Mutual Information, as expounded in Claude Shannon's Information Theory [36] to measure the probability of commonality between two data distributions which may not be parametric. In real-world applications, BN excels in counterfactual simulations of conditions and their effects when uncertainty persists [37].…”