Prediction uncertainty and data worth assessment for groundwater transport times in an agricultural catchment

Zell, W.; Culver, Teresa B.; Sanford, Ward E.

doi:10.1016/j.jhydrol.2018.02.006

Cited by 19 publications

(32 citation statements)

References 62 publications

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“…The forecast-specific nature of observation worth has also been reported previously (e.g., Dausman et al, 2010;Fienen et al, 2010;White et al, 2016). The worth of MRT observations relative to various hydraulic potential and discharge observations across the different forecasts are, in general terms, similar to those reported by Hunt et al (2006), Masbruch et al (2014), Oehlmann et al (2015), and Zell et al (2018) (especially when considering the discussion point in the following paragraph).…”

Section: Resultssupporting

confidence: 79%

“…Furthermore, we expect the above-mentioned issues associated with imperfect-model data assimilation to be relevant and largely transferrable to the assimilation of other environmental tracers, other information-rich observations and diverse data types more generally. This is because we consider the primary barrier to the appropriate assimilation of tritium observation data encountered in the second case study to be fundamental challenges associated with extracting appropriate information from spatially discrete concentration observations when using upscaled or simplified representations of hydraulic properties within a regional-scale model that simulates tracer concentrations using the advection-dispersion equation (e.g., Zheng and Gorelick, 2003;Riva et al, 2008). To the extent that simulated outputs corresponding to observed tracer concentrations are sensitive to model details or parameters that are "missing" in a simplified model (e.g., White et al, 2014), parameter compensation will occur (e.g., Clark and Vrugt, 2006).…”

Section: Resultsmentioning

confidence: 99%

“…In a review of approaches for modeling environmental tracers in groundwater systems, Turnadge and Smerdon (2014) state that age data have been useful for constraining models; in particular, "simulation of environmental tracer transport that explicitly accounts for the accumulation and decay of tracer mass, has proven to be highly beneficial in constraining numerical models". Zell et al (2018) showed the relative importance of water level, stream discharge and environmental tracers (including tritium, chlorofluorocarbons -CFCs and SF6) in the conditioning of groundwater travel time forecasts. They reported that, overall, tracer data were of considerable benefit in terms of forecast uncertainty reduction.…”

Section: Introductionmentioning

confidence: 99%

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On the assimilation of environmental tracer observations for model-based decision support

Knowling

White

Moore

et al. 2020

Hydrol. Earth Syst. Sci.

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It has been advocated that history matching numerical models to a diverse 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 regionalscale 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 tritiumderived 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 diverse 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 diverse 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.

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Section: Resultssupporting

confidence: 79%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the assimilation of environmental tracer observations for model-based decision support

Knowling

White

Moore

et al. 2020

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…We agree that the specific finding from the first case study that the spring discharge observations are of most "worth" when considering spring discharge forecasts is not surprising. We state this explicitly on Lines 243-245: "The worth of MRT observations relative to various hydraulic potential and discharge observations across the different forecasts are, in general terms, similar to those reported by Zell et al (2018)". To address this comment, we will add the Hunt et al 2006…”

mentioning

confidence: 76%

“…To be clear, no actual parameter estimation is undertaken as part of the first case study. FOSM techniques have been widely employed for data worth assessment in this notional context in many settings as it enables rapid exploration of the worth of many different combinations of conditional forecast variances in a computationally efficient manner (e.g., Wallis et al, 2014;Zell et al, 2018). We will address this comment by…”

Section: Discussion Papermentioning

confidence: 99%

Reply to Anonymous Referee #2

Knowling¹

2019

Preprint

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Quantifying subsurface parameter and transport uncertainty using surrogate modelling and environmental tracers

Thiros

Gardner

Maneta

et al. 2022

Hydrological Processes

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We combine physics‐based groundwater reactive transport modelling with machine‐learning techniques to quantify hydrogeological model and solute transport predictive uncertainties. We train an artificial neural network (ANN) on a dataset of groundwater hydraulic heads and 3H concentrations generated using a high‐fidelity groundwater reactive transport model. Using the trained ANN as a surrogate model to reproduce the input–output response of the high‐fidelity reactive transport model, we quantify the posterior distributions of hydrogeological parameters and hydraulic forcing conditions using Markov chain Monte Carlo calibration against field observations of groundwater hydraulic heads and 3H concentrations. We demonstrate the methodology with a model application that predicts Chlorofluorocarbon‐12 (CFC‐12) solute transport at a contaminated field site in Wyoming, United States. Our results show that including 3H observations in the calibration dataset reduced the uncertainty in the estimated permeability field and infiltration rates, compared to calibration against hydraulic heads alone. However, predictive uncertainty quantification shows that CFC‐12 transport predictions conditioned to the parameter posterior distributions cannot reproduce the field measurements. We found that calibrating the model to hydraulic head and 3H observations results in groundwater mean ages that are too large to explain the observed CFC‐12 concentrations. The coupling of the physics‐based reactive transport model with the machine‐learning surrogate model allows us to efficiently quantify model parameter and predictive uncertainties, which is typically computationally intractable using reactive transport models alone.

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Prediction uncertainty and data worth assessment for groundwater transport times in an agricultural catchment

Cited by 19 publications

References 62 publications

On the assimilation of environmental tracer observations for model-based decision support

On the assimilation of environmental tracer observations for model-based decision support

Reply to Anonymous Referee #2

Quantifying subsurface parameter and transport uncertainty using surrogate modelling and environmental tracers

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