A Bayesian belief network approach for assessing uncertainty in conceptual site models at contaminated sites

Thomsen, Nanna Isbak; Binning, Philip John; McKnight, Ursula S.; Tuxen, Nina; Bjerg, Poul Løgstrup; Troldborg, Mads

doi:10.1016/j.jconhyd.2016.02.003

Cited by 17 publications

(6 citation statements)

References 60 publications

(86 reference statements)

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“…In hydrogeology the forward model is most often a numerical groundwater model (Remson et al, 1980) or solute transport model (Thomsen et al, 2016; Troldborg et al, 2010), but the forward model can also be a geophysical forward model (Brunetti et al, 2017; Hermans et al, 2015) or a water balance model (Enemark, Peeters, Mallants, Batelaan, Valentine, & Sambridge, 2019).…”

Section: Methodsmentioning

confidence: 99%

A Systematic Approach to Hydrogeological Conceptual Model Testing, Combining Remote Sensing and Geophysical Data

Enemark

Peeters

Mallants

et al. 2020

Water Resources Research

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Conceptual uncertainty is considered one of the major sources of uncertainty in groundwater flow modeling. Hypothesis testing is essential to increase system understanding by analyzing and refuting alternative conceptual models. We present a systematic approach to conceptual model testing aimed at finding an ensemble of conceptual understandings consistent with prior knowledge and observational data. This differs from the traditional approach of tuning the parameters of a single conceptual model to conform with the data through inversion. We apply this approach to a simplified hydrogeological characterization of the Wildman River area (Northern Territory, Australia) and evaluate the connectivity of sinkhole‐type depressions to groundwater. Alternative models are developed representing the process structure (i.e., different fluxes representing interactions between surface water and groundwater) and physical structure (i.e., different lithologies underlying the depressions) of the conceptual model of the depressions. Remote sensing data are used to test the process structure, while geophysical data are used to test the physical structure. Both data types are used to remove inconsistent models from an ensemble of 16 models and to update the probability of the remaining alternative conceptual models. Three out of five depressions that are used as a test case are conditionally confirmed to act as conduits for recharge, while for the last two depressions, the data are inconclusive. Although the framework is not directly prediction oriented, the testing of plausible conceptual models will ultimately lead to increased confidence of any groundwater model based on accepted posterior conceptualizations.

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Section: Methodsmentioning

confidence: 99%

A Systematic Approach to Hydrogeological Conceptual Model Testing, Combining Remote Sensing and Geophysical Data

Enemark

Peeters

Mallants

et al. 2020

Water Resources Research

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“…We used the Shannon entropy [H(X)] (Equation 1) to calculate uncertainty in the BBN. Shannon entropy indicates the average information conveyed by data sources used to develop a BBN and H(X) values vary between 0 and 1, with high values indicating variables in the BBN requiring additional information to improve projections (Uusiitalo et al, 2015;Thomsen et al, 2016;Pagano et al, 2018).…”

Section: Bayesian Belief Network Designmentioning

confidence: 99%

Peat Carbon Vulnerability to Projected Climate Warming in the Hudson Bay Lowlands, Canada: A Decision Support Tool for Land Use Planning in Peatland Dominated Landscapes

McLaughlin¹,

Packalen

2021

Front. Earth Sci.

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Peatlands help regulate climate by sequestering (net removal) carbon from the atmosphere and storing it in plants and soils. However, as mean annual air temperature (MAAT) increases, peat carbon stocks may decrease. We conducted an in-depth synthesis of current knowledge about ecosystem controls on peatland carbon storage and fluxes to constrain the most influential parameters in probabilistic modelling of peat carbon sinks, such as Bayesian belief networks. Evaluated parameters included climate, carbon flux and mass, land cover, landscape position (defined here as elevation), fire records, and current and future climate scenarios for a 74,300 km2 landscape in the Hudson Bay Lowlands, Canada. The Bayesian belief network was constructed with four tiers: 1) exposure, expressed as MAAT, and the state variables of elevation and land cover; 2) sensitivity, expressed as ecosystem conditions relevant to peat carbon mass and its quality for decomposition, peat wetness, and fire; 3) carbon dioxide and methane fluxes and peat combustion; and 4) vulnerability of peat carbon sink strength under warmer MAAT. Simulations were conducted using current (−3.0 to 0.0°C), moderately warmer (0.1–4.0°C), and severely warmer (4.1–9.0°C) climate scenarios. Results from the severely warmer climate scenario projected an overall drying of peat, with approximately 20% reduction in the strong sink categories of net ecosystem exchange and peat carbon sink strength for the severely and, to a lesser degree, the moderately warmer climate scenarios relative to current MAAT. In the warmest temperature simulation, probability of methane emission decreased slightly and the probability of the strong peat carbon sink strength was 27% lower due to peat combustion. Our Bayesian belief network can assist land planners in decision-making for peatland-dominated landscapes, such as identifying high carbon storage areas and those projected to be at greatest risk of carbon loss due to climate change. Such areas may be designated, for example, as protected or reduced management intensity. The Bayesian belief network presented here is built on an in-depth knowledge synthesis to construct conditional probability tables, so is expected to apply to other peatland-dense jurisdictions by changing only elevation, peatland types, and MAAT.

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“…Risk assessment can be used to determine levels of harm, to prioritise issues, or to inform policy, and comprises a series of logical steps: identification, definition of scope, development of a conceptual model, assessment, characterisation, management, communication, that enable the assessment of any environmental activity (Defra, 2011). A significant step in the risk assessment process is the development of the conceptual site model (CSM), which is used to establish the links between source-pathway-receptors (Simon et al, 2016;Thomsen et al, 2016). Within the risk assessment framework, social, environment, and economic values are often considered to provide a holistic perspective.…”

Section: What Is An Integrated Risk Assessment Framework?mentioning

confidence: 99%

Working towards an integrated land contamination management framework for Nigeria

Sam

Coulon

Prpich

2016

Science of The Total Environment

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Over the past five decades, Nigeria has developed a number of contaminated land legislations to address the damage caused primarily by oil and gas exploitation activities. Within these legislations exists elements of risk assessment and risk-based corrective action. Despite this progress, we argue that contaminated land management approaches in Nigeria need further development to be able to integrate new scientific information, and to address environmental, economic, and social values. By comparison, advanced contaminated land regimes in the United Kingdom (UK), the Netherlands, Australia, New Zealand, and the United States of America (USA) apply a number of integrative approaches (e.g. sustainability appraisal, liability regime, funding mechanisms, technology demonstration) that enable them to meet the environmental, economic, and social needs of their populations. In comparison, Nigerian governance lacks many of these mechanisms and management of contaminated land is ad hoc. In this paper we propose an integrated risk assessment framework for Nigeria that incorporates the principles of sustainability and stakeholder engagement into the decision-making processes for contaminated land risk assessment and risk management. The integrated approach relies on transparency to promote acceptance and build trust in institutions, and uses stakeholder engagement to address data deficiencies. We conclude this paper with a roadmap for how Nigeria might implement such an integrative approach into their existing contaminated land regulatory system, as well as identify a series of policy priorities that should be addressed.

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A Bayesian belief network approach for assessing uncertainty in conceptual site models at contaminated sites

Cited by 17 publications

References 60 publications

A Systematic Approach to Hydrogeological Conceptual Model Testing, Combining Remote Sensing and Geophysical Data

A Systematic Approach to Hydrogeological Conceptual Model Testing, Combining Remote Sensing and Geophysical Data

Peat Carbon Vulnerability to Projected Climate Warming in the Hudson Bay Lowlands, Canada: A Decision Support Tool for Land Use Planning in Peatland Dominated Landscapes

Working towards an integrated land contamination management framework for Nigeria

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