Dynamic Bayesian Networks to Assess Anthropogenic and Climatic Drivers of Saltwater Intrusion: A Decision Support Tool Toward Improved Management

Rachid, G.; Alameddine, Ibrahim; Najm, Majdi Abou; Qian, Song S.; El‐Fadel, Mutasem

doi:10.1002/ieam.4355

Cited by 14 publications

(3 citation statements)

References 103 publications

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“…Moreover, BNs cannot include feedback loops, although adaptations can be made. The BN models proposed here can be further advanced in several directions, for example, dynamic BNs for future projections allowing feedback loops (Gaasland‐Tatro, 2016), spatially explicit BN models with links to geographical information systems (Piffady et al, 2021), cumulative risk of multiple stressors calculated as the joint probability of threshold exceedances, adapted to the specific type of stressor interaction (Welch, 2023), and decision‐support tools with decision nodes and cost/benefit nodes (influence diagrams) (Rachid et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach

Moe,

Brix,

Landis

et al. 2024

Integr Envir Assess & Manag

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The Society of Environmental Toxicology and Chemistry (SETAC) convened a Pellston workshop in 2022 to examine how information on climate change could be better incorporated into the ecological risk assessment (ERA) process for chemicals as well as other environmental stressors. A major impetus for this workshop is that climate change can affect components of ecological risks in multiple direct and indirect ways, including the use patterns and environmental exposure pathways of chemical stressors such as pesticides, the toxicity of chemicals in receiving environments, and the vulnerability of species of concern related to habitat quality and use. This paper explores a modeling approach for integrating climate model projections into the assessment of near‐ and long‐term ecological risks, developed in collaboration with climate scientists. State‐of‐the‐art global climate modeling and downscaling techniques may enable climate projections at scales appropriate for the study area. It is, however, also important to realize the limitations of individual global climate models and make use of climate model ensembles represented by statistical properties. Here, we present a probabilistic modeling approach aiming to combine projected climatic variables as well as the associated uncertainties from climate model ensembles in conjunction with ERA pathways. We draw upon three examples of ERA that utilized Bayesian networks for this purpose and that also represent methodological advancements for better prediction of future risks to ecosystems. We envision that the modeling approach developed from this international collaboration will contribute to better assessment and management of risks from chemical stressors in a changing climate.

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Section: Discussionmentioning

confidence: 99%

Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach

Moe,

Brix,

Landis

et al. 2024

Integr Envir Assess & Manag

View full text Add to dashboard Cite

show abstract

“…In addition to the possible monitoring and projection applications of our fixed model, a more detailed, explicitly dynamic BN model could be developed to quantify cumulative change across time steps. Dynamic BN models use time series probabilistic inference and still provide the advantages of network-based causal analyses under system uncertainty [80][81][82][83].…”

Section: Discussionmentioning

confidence: 99%

Wildfire Risk Assessment for Strategic Forest Management in the Southern United States: A Bayesian Network Modeling Approach

Nepal,

Pomara,

Gould

et al. 2023

Land

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Wildfire occurrences have increased and are projected to continue increasing globally. Strategic, evidence-based planning with diverse stakeholders, making use of diverse ecological and social data, is crucial for confronting and mitigating the associated risks. Prescribed fire, when planned and executed carefully, is a key management tool in this effort. Assessing where prescribed fire can be a particularly effective forest management tool can help prioritize efforts, reduce wildfire risk, and support fire-resilient lands and communities. We collaborated with expert stakeholders to develop a Bayesian network model that integrated a large variety of biophysical, socioecological, and socioeconomic spatial information for the Southeastern United States to quantify where risk is high and where prescribed fire would be efficient in mitigating risk. The model first estimated wildfire risk based on landscape-scale interactions among the likelihoods of fire occurrence and severity and the people and resources potentially exposed—accounting for socioeconomic vulnerabilities as well as key ecosystem services. The model then quantified the potential for risk reduction through prescribed fire, given the existing fuel load, climate, and other landscape conditions. The resulting expected risk estimates show high risk concentrated in the coastal plain and interior highland subregions of the Southern US, but there was considerable variation among risks to different ecosystem services and populations, including potential exposure to smoke emissions. The capacity to reduce risk through fuel reductions was spatially correlated with risk; where these diverged, the difference was largely explained by fuel load. We suggest that both risk and the capacity for risk reduction are important in identifying priorities for management interventions. The model serves as a decision support tool for stakeholders to coordinate large-landscape adaptive management initiatives in the Southern US. The model is flexible with regard to both empirical and expert-driven parameterizations and can be updated as new knowledge and data emerge. The resulting spatial information can help connect active management options to forest management goals and make management more efficient through targeted investments in priority landscapes.

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“…We propose that the probabilistic approach presented in this work may help to address these uncertainties in future studies, thanks to the ability to incorporate diverse data from a range of sources, whilst propagating the uncertainty. A dynamic BBN (e.g., Rachid et al, 2021) can then be developed to represent P fluxes over shorter or longer timescales.…”

Section: Developing and Testing A Probabilistic Systems-based Decisio...mentioning

confidence: 99%

A systems approach to modelling phosphorus pollution risk in Scottish rivers using a spatial Bayesian Belief Network helps targeting effective mitigation measures

Glendell

Gagkas

Stutter

et al. 2022

Front. Environ. Sci.

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Water quality remains a main reason for the failure of waterbodies to reach Good Ecological Status (GES) under the European Union Water Framework Directive (WFD), with phosphorus (P) pollution being a major cause of water quality failures. Reducing P pollution risk in agricultural catchments is challenging due to the complexity of biophysical drivers along the source-mobilisation-delivery-impact continuum. While there is a need for place-specific interventions, the evidence supporting the likely effectiveness of mitigation measures and their spatial targeting is uncertain. We developed a decision-support tool using a Bayesian Belief Network that facilitates system-level thinking about P pollution and brings together academic and stakeholder communities to co-construct a model appropriate to the region of interest. The expert-based causal model simulates the probability of soluble reactive phosphorus (SRP) concentration falling into the WFD high/good or moderate/poor status classifications along with the effectiveness of three mitigation measures including buffer strips, fertiliser input reduction and septic tank management. In addition, critical source areas of pollution are simulated on 100 × 100 m raster grids for seven catchments (12–134 km2) representative of the hydroclimatic and land use intensity gradients in Scotland. Sensitivity analysis revealed the importance of fertiliser inputs, soil Morgan P, eroded SRP delivery rate, presence/absence of artificial drainage and soil erosion for SRP losses from diffuse sources, while the presence/absence of septic tanks, farmyards and the design size of sewage treatment works were influential variables related to point sources. Model validation confirmed plausible model performance as a “fit for purpose” decision support tool. When compared to observed water quality data, the expert-based causal model simulated a plausible probability of GES, with some differences between study catchments. Reducing fertiliser inputs below optimal agronomic levels increased the probability of GES by 5%, while management of septic tanks increased the probability of GES by 8%. Conversely, implementation of riparian buffers did not have an observable effect on the probability of GES at the catchment outlet. The main benefit of the approach was the ability to integrate diverse, and often sparse, information; account for uncertainty and easily integrate new data and knowledge.

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Dynamic Bayesian Networks to Assess Anthropogenic and Climatic Drivers of Saltwater Intrusion: A Decision Support Tool Toward Improved Management

Abstract: This article is part of the special series "Applications of Bayesian Networks for Environmental Risk Assessment and Management" and was generated from a session on the use of Bayesian networks (BNs) in environmental modeling and assessment in 1 of 3 recent conferences:

Cited by 14 publications

References 103 publications

Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach

Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach

Wildfire Risk Assessment for Strategic Forest Management in the Southern United States: A Bayesian Network Modeling Approach

A systems approach to modelling phosphorus pollution risk in Scottish rivers using a spatial Bayesian Belief Network helps targeting effective mitigation measures

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