A Bayesian Networks approach for the assessment of climate change impacts on nutrients loading

Sperotto, Anna; Molina, José-Luis; Torresan, Silvia; Critto, Andrea; Pulido-Velázquez, Manuel; Marcomini, Antonio

doi:10.1016/j.envsci.2019.06.004

Cited by 43 publications

(25 citation statements)

References 58 publications

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“…A BN model was employed to assess and compare the impacts of different climate change scenarios on nutrient loadings (i.e., NO 3 − , NH 4 + , PO 4 3− ) in the transitional waters of the Zero river basin, thus generating an ensemble of impact scenarios supporting the identification of climate change effect on water quality. The BN was implemented by building on a BN model previously developed and validated in a case study [36] which was extended to allow the incorporation of multiple GCM-RCM inputs. The BN for the Zero river basin was developed and run using the software HUGIN Expert, version 8 [37,38].…”

Section: Bayesian Network Modelmentioning

confidence: 99%

“…The BN for the Zero river basin was developed and run using the software HUGIN Expert, version 8 [37,38]. For additional details about the methodology and data used to develop the BN, please refer to Reference [36].…”

Section: Bayesian Network Modelmentioning

confidence: 99%

“…The BN structure was designed following the DPSIR (Drivers-Pressures-State-Impacts-Responses) framework, starting from the conceptual model described in Reference [36]. An influence (i.e., "box and arrow") diagram was developed including the most relevant systems variables (i.e., nodes), as well as the links between them (i.e., directed arcs), allowing the identification of the main cause-effect pathways between input variables, represented by climatic changes and land use, and output variables, represented by the increase in nutrient loadings (i.e., NO 3 − , NH 4 + , PO 4 3− ) discharged by the Zero river basin into the Venice lagoon.…”

Section: Bn Development and Trainingmentioning

confidence: 99%

“…Finally, nodes describing agronomic practices (i.e., water needs, irrigation, P and N fertilizer application) were trained through expert elicitation or by applying empirical equations due to the lack of quantitative information and experiences in the case study. An exhaustive description of assumptions and information used to train Sustainability 2019, 11, 4764 7 of 34 the BN can be found in Reference [36]. Figure 3 shows the configuration of the BN for the Zero river basin once states, prior information, and conditional probabilities of each node were parametrized.…”

Section: Bn Development and Trainingmentioning

confidence: 99%

“…The developed BN was evaluated with the main aim to assess if it was able to correctly represent nutrient loadings and dynamics in the case study. Specifically, two main forms of model evaluation were performed, namely, model predictive accuracy and sensitivity analysis [36].…”

Section: Bn Evaluationmentioning

confidence: 99%

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

et al. 2019

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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...

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Section: Bayesian Network Modelmentioning

confidence: 99%

Section: Bayesian Network Modelmentioning

confidence: 99%

Section: Bn Development and Trainingmentioning

confidence: 99%

Section: Bn Development and Trainingmentioning

confidence: 99%

Section: Bn Evaluationmentioning

confidence: 99%

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

et al. 2019

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Irrigation water fitness assessment based on Bayesian network and FAO guidelines

Ilić

Mutavdžić

Srđević

et al. 2022

Irrigation and Drainage

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The use of inappropriate irrigation water may lead to unwanted consequences for the soil, plants, irrigation system equipment and more. Ideally, the assessment of water fitness should include all influential factors and their interdependence. Because that is rarely the case, as an attempt to satisfy such requirements we propose a new approach based on the FAO guidelines and Bayesian network (BN) modelling. Fifteen factors recommended by the FAO were used to create a network to assess irrigation water fitness. The usability of the BN was tested on two water samples from two measuring points in Vojvodina Province, Serbia. The results obtained from the same sample are diverse and depend on the plants' tolerance to salt and ionic toxicity, and on soil texture. For the different conditions at the measuring point Hetin, the probability of no restriction on use varied from 50 to 84%. In the case of the Bačko Gradište measuring point, the probability varied from 66 to 91.5%, and the strongest fitness depends on the soil texture. Comparison of the obtained results with the FAO classification shows high correlation and points to flexibility and adaptability of the created network to change data or working with missing data.

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Machine Learning with Meteorological Variables for the Prediction of the Electric Field in East Lima, Peru

Soria

Poma

Sumire

et al. 2021

Proceedings of Sixth International Congress on Information and Communication Technology

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A Bayesian Networks approach for the assessment of climate change impacts on nutrients loading

Cited by 43 publications

References 58 publications

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

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

Irrigation water fitness assessment based on Bayesian network and FAO guidelines

Machine Learning with Meteorological Variables for the Prediction of the Electric Field in East Lima, Peru

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