ORCID Profile
0000-0002-7273-3495
Current Organisation
University of Adelaide
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Publisher: Elsevier BV
Date: 05-2021
Publisher: Elsevier BV
Date: 06-2019
Publisher: Elsevier BV
Date: 07-2023
Publisher: Elsevier BV
Date: 04-2015
Publisher: Frontiers Media SA
Date: 28-02-2020
Publisher: Elsevier BV
Date: 10-2022
Publisher: Elsevier BV
Date: 07-2013
Publisher: Frontiers Media SA
Date: 27-11-2020
DOI: 10.3389/FEART.2020.565613
Abstract: Effective decision making for resource management is often supported by combining predictive models with uncertainty analyses. This combination allows quantitative assessment of management strategy effectiveness and risk. Typically, history matching is undertaken to increase the reliability of model forecasts. However, the question of whether the potential benefit of history matching will be realized, or outweigh its cost, is seldom asked. History matching adds complexity to the modeling effort, as information from historical system observations must be appropriately blended with the prior characterization of the system. Consequently, the cost of history matching is often significant. When it is not implemented appropriately, history matching can corrupt model forecasts. Additionally, the available data may offer little decision-relevant information, particularly where data and forecasts are of different types, or represent very different stress regimes. In this paper, we present a decision support modeling workflow where early quantification of model uncertainty guides ongoing model design and deployment decisions. This includes providing justification for undertaking (or forgoing) history matching, so that unnecessary modeling costs can be avoided and model performance can be improved. The workflow is demonstrated using a regional-scale modeling case study in the Wairarapa Valley (New Zealand), where assessments of stream depletion and nitrate-nitrogen contamination risks are used to support water-use and land-use management decisions. The probability of management success/failure is assessed by comparing the proximity of model forecast probability distributions to ecologically motivated decision thresholds. This study highlights several important insights that can be gained by undertaking early uncertainty quantification, including: i) validation of the prior numerical characterization of the system, in terms of its consistency with historical observations ii) validation of model design or indication of areas of model shortcomings iii) evaluation of the relative proximity of management decision thresholds to forecast probability distributions, providing a justifiable basis for stopping modeling.
Publisher: Elsevier BV
Date: 03-2022
Publisher: Wiley
Date: 12-05-2017
DOI: 10.1111/GWAT.12526
Abstract: The estimation of recharge through groundwater model calibration is h ered by the nonuniqueness of recharge and aquifer parameter values. It has been shown recently that the estimability of spatially distributed recharge through calibration of steady-state models for practical situations (i.e., real-world, field-scale aquifer settings) is limited by the need for excessive amounts of hydraulic-parameter and groundwater-level data. However, the extent to which temporal recharge variability can be informed through transient model calibration, which involves larger water-level datasets, but requires the additional consideration of storage parameters, is presently unknown for practical situations. In this study, time-varying recharge estimates, inferred through calibration of a field-scale highly parameterized groundwater model, are systematically investigated subject to changes in (1) the degree to which hydraulic parameters including hydraulic conductivity (K) and specific yield (S
Publisher: Oxford University Press (OUP)
Date: 18-09-2019
DOI: 10.1111/IJPP.12580
Abstract: To investigate how community pharmacists view their responsibility for patient care in a scenario involving opioid use with significant risk of toxicity or misadventure. A case scenario was developed based on an Australian coronial inquiry involving a patient suffering fatal toxicity following misuse of opioids. Community pharmacists working in Brisbane, Queensland, were invited to take part in face-to-face semi-structured interviews at their place of work. Participants were asked how they would respond to the scenario in practice and their perceived responsibilities. Twenty-one pharmacists were interviewed. Participants identified similar actions in response to the case, and potential barriers and enablers. Participants differed with regard to how they described their perceived scope of practice and degree of responsibility in response to the case. Most participants described their scope of practice in terms of medication management with a focus on patient outcomes. Some participants described a narrower scope of practice that focused on either medicine supply or legal aspects. Participants who described a medication management focus differed in their views regarding their responsibility for patient outcomes in the case. Pharmacists in this study varied in terms of their perceived scope of practice and responsibility to patient outcomes in response to a case involving a patient at risk of opioid-related harm. Further work on pharmacist responsibility may reduce this variability.
Publisher: Elsevier BV
Date: 11-2020
Publisher: Copernicus GmbH
Date: 08-04-2020
DOI: 10.5194/HESS-24-1677-2020
Abstract: Abstract. It has been advocated that history matching numerical models to a erse range of observation data types, particularly including environmental tracer concentrations and their interpretations and derivatives (e.g., mean age), constitutes an effective and appropriate means to improve model forecast reliability. This study presents two regional-scale modeling case studies that directly and rigorously assess the value of discrete tritium concentration observations and tritium-derived mean residence time (MRT) estimates in two decision-support contexts “value” is measured herein as both the improvement (or otherwise) in the reliability of forecasts through uncertainty variance reduction and bias minimization as a result of assimilating tritium or tritium-derived MRT observations. The first case study (Heretaunga Plains, New Zealand) utilizes a suite of steady-state and transient flow models and an advection-only particle-tracking model to evaluate the worth of tritium-derived MRT estimates relative to hydraulic potential, spring discharge and river–aquifer exchange flux observations. The worth of MRT observations is quantified in terms of the change in the uncertainty surrounding ecologically sensitive spring discharge forecasts via first-order second-moment (FOSM) analyses. The second case study (Hauraki Plains, New Zealand) employs paired simple–complex transient flow and transport models to evaluate the potential for assimilation-induced bias in simulated surface-water nitrate discharge to an ecologically sensitive estuary system formal data assimilation of tritium observations is undertaken using an iterative ensemble smoother. The results of these case studies indicate that, for the decision-relevant forecasts considered, tritium observations are of variable benefit and may induce damaging bias in forecasts these biases are a result of an imperfect model's inability to properly and directly assimilate the rich information content of the tritium observations. The findings of this study challenge the advocacy of the increasing use of tracers, and of erse data types more generally, whenever environmental model data assimilation is undertaken with imperfect models. This study also highlights the need for improved imperfect-model data assimilation strategies. While these strategies will likely require increased model complexity (including advanced discretization, processes and parameterization) to allow for appropriate assimilation of rich and erse data types that operate across a range of spatial and temporal scales commensurate with a forecast of management interest, it is critical that increased model complexity does not preclude the application of formal data assimilation and uncertainty quantification techniques due to model instability and excessive run times.
Publisher: Wiley
Date: 20-11-2019
DOI: 10.1111/GWAT.12957
Abstract: One of the first and most important decisions facing practitioners when constructing a numerical groundwater model is vertical discretization. Several factors will influence this decision, such as the conceptual model of the system and hydrostratigraphy, data availability, resulting computational burden, and the purpose of the modeling analysis. Using a coarse vertical discretization is an attractive option for practitioners because it reduces data requirements and model construction efforts, can increase model stability, and can reduce computational demand. However, using a coarse vertical discretization as a form of model simplification is not without consequence this may give rise to unwanted side-effects such as biases in decision-relevant simulated outputs. Given its foundational role in the modeled representation of the aquifer system, herein we investigate how vertical discretization may affect decision-relevant simulated outputs using a paired complex-simple model analysis. A Bayesian framework and decision analysis approach are adopted. Two case studies are considered, one of a synthetic, linked unsaturated-zone/surface-water/groundwater hydrologic model and one of a real-world linked surface-water/groundwater hydrologic-nitrate transport model. With these models, we analyze decisions related to abstraction-induced changes in ecologically important streamflow characteristics and differences in groundwater and surface-water nitrate concentrations and mass loads following potential land-use change. We show that for some decision-relevant simulated outputs, coarse vertical discretization induces bias in important simulated outputs, and can lead to incorrect resource management action. For others, a coarse vertical discretization has little or no consequence for resource management decision-making.
Publisher: No publisher found
Date: 2020
Publisher: Elsevier BV
Date: 09-2016
Publisher: Elsevier BV
Date: 05-2020
Publisher: Copernicus GmbH
Date: 18-09-2019
Publisher: Copernicus GmbH
Date: 18-09-2019
Abstract: Abstract. It has been advocated that history-matching numerical models to a erse range of observation data types, particularly including environmental tracer concentrations and their interpretations/derivatives (e.g., mean age), constitutes an effective and appropriate means to improve model forecast reliability. This study presents two regional-scale modeling case studies that directly and rigorously assess the value of discrete tritium concentration observations and tritium-derived mean residence time (MRT) estimates in two decision-support contexts value herein is measured as the improvement (or otherwise) in the reliability of forecasts through uncertainty variance reduction and bias minimization as a result of assimilating tritium or tritium-derived MRT observations. The first case study (Heretaunga Plains, New Zealand) utilizes a suite of steady-state and transient flow models and an advection-only particle-tracking model to evaluate the worth of tritium-derived MRT estimates relative to hydraulic potential, spring discharge and river/aquifer exchange flux observations. The worth of MRT observations is quantified in terms of the change in the uncertainty surrounding ecologically-sensitive spring discharge forecasts via first-order second-moment analyses. The second case study (Hauraki Plains, New Zealand) employs paired simple/complex transient flow and transport models to evaluate the potential for assimilation-induced bias in simulated surface-water nitrate discharge to an ecologically-sensitive estuary system formal data assimilation of tritium observations is undertaken using an iterative ensemble smoother. The results of these case studies indicate that, for the decision-relevant forecasts considered, tritium observations are of variable benefit and may induce damaging bias in forecasts these biases are a result of an imperfect model's inability to properly and directly assimilate the rich information content of the tritium observations. The findings of this study challenge the unqualified advocacy of the increasing use of tracers, and erse data types more generally, whenever environmental model data assimilation is undertaken with imperfect models. This study also highlights the need for improved imperfect-model data assimilation strategies. While these strategies will likely require increased model complexity (including advanced discretization, processes and parameterization) to allow for appropriate assimilation of rich and erse data types that operate across a range of spatial and temporal scales commensurate with a forecast of management interest, it is critical that increased model complexity does not preclude the application of formal data assimilation and uncertainty quantification techniques due to model instability and excessive run times.
Publisher: Elsevier BV
Date: 10-2026
No related grants have been discovered for Matthew Knowling.