ORCID Profile
0000-0002-4950-1469
Current Organisation
Intera Inc
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Publisher: Elsevier BV
Date: 05-2021
Publisher: Elsevier BV
Date: 10-2022
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: 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.
No related grants have been discovered for Jeremy White.