Debates—Stochastic subsurface hydrology from theory to practice: The relevance of stochastic subsurface hydrology to practical problems of contaminant transport and remediation. What is characterization and stochastic theory good for?

Fiori, Aldo; Cvetković, Vladimir; Dagan, Gédéon; Attinger, Sabine; Bellin, Alberto; Dietrich, Peter; Zech, Alraune; Teutsch, Georg

doi:10.1002/2015wr017525

Cited by 41 publications

(41 citation statements)

References 36 publications

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“…Stochastic methods have been developed by hydrologists to describe solute transport in heterogeneous media for approximately 40 years, but these models usually exhibit poor predictability and lack hydrogeologic information; see extensive discussion by Cirpka and Valocchi (); Fiori et al (); Fogg and Zhang (); Rajaram (); and Sanchez‐Vila and Fernandez‐Garcia (). Our study found that the time nonlocal transport model (with constant parameters), as a popular stochastic model, might be challenged further by scale‐dependent dispersion.…”

Section: Discussionmentioning

confidence: 99%

Can a Time Fractional‐Derivative Model Capture Scale‐Dependent Dispersion in Saturated Soils?

et al. 2017

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Time nonlocal transport models such as the time fractional advection-dispersion equation (t-fADE) were proposed to capture well-documented non-Fickian dynamics for conservative solutes transport in heterogeneous media, with the underlying assumption that the time nonlocality (which means that the current concentration change is affected by previous concentration load) embedded in the physical models can release the effective dispersion coefficient from scale dependency. This assumption, however, has never been systematically examined using real data. This study fills this historical knowledge gap by capturing non-Fickian transport (likely due to solute retention) documented in the literature (Huang et al. 1995) and observed in our laboratory from small to intermediate spatial scale using the promising, tempered t-fADE model. Fitting exercises show that the effective dispersion coefficient in the t-fADE, although differing subtly from the dispersion coefficient in the standard advection-dispersion equation, increases nonlinearly with the travel distance (varying from 0.5 to 12 m) for both heterogeneous and macroscopically homogeneous sand columns. Further analysis reveals that, while solute retention in relatively immobile zones can be efficiently captured by the time nonlocal parameters in the t-fADE, the motion-independent solute movement in the mobile zone is affected by the spatial evolution of local velocities in the host medium, resulting in a scale-dependent dispersion coefficient. The same result may be found for the other standard time nonlocal transport models that separate solute retention and jumps (i.e., displacement). Therefore, the t-fADE with a constant dispersion coefficient cannot capture scale-dependent dispersion in saturated porous media, challenging the application for stochastic hydrogeology methods in quantifying real-world, preasymptotic transport. Hence improvements on time nonlocal models using, for example, the novel subordination approach are necessary to incorporate the spatial evolution of local velocities without adding cumbersome parameters.

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Section: Discussionmentioning

confidence: 99%

Can a Time Fractional‐Derivative Model Capture Scale‐Dependent Dispersion in Saturated Soils?

et al. 2017

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“…At the Basel site, the subsurface uncertainty strongly influences the flow gradients and spreading of the pathlines as reflected in the pathline density distributions. This finding can have significant implications for field work and water management strategies (McKenna and Wahi 2006; Devlin and Schillig 2017) as well as for transport of pollutants in the subsurface (Fiori et al 2016;Frind and Molson 2018). The NSMC analysis can be combined with various models and model predictions (e.g., Tonkin and Doherty 2009;Moeck et al 2018) and does not necessarily include PT as applied in this study.…”

Section: Nsmc Analysis With Pilot Point Approachmentioning

confidence: 99%

Pathline Density Distributions in a Null‐Space Monte Carlo Approach to Assess Groundwater Pathways

Moeck¹,

Molson

Schirmer

2019

Groundwater

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A null‐space Monte‐Carlo (NSMC) approach was applied to account for uncertainty in the calibration of the hydraulic conductivity (K) field for a three‐dimensional groundwater flow model of a major water supply system in Switzerland. The approach generates different parameter realizations of the K field using the pilot point methodology. Subsequently, particle tracking (PT) was applied to each calibrated model, and the resulting particles are interpreted as the spatial pathline density distribution of multiple sources. The adopted approach offers advantages over classical PT which does not provide a means for treating uncertainty originating from the incomplete description of subsurface heterogeneity. Uncertainty in the K field is shown to strongly influence the spatial pathline distribution. Pathline spreading is particularly evident in locations where the information content of the head observations does not sufficiently constrain the estimated parameters. Despite the predictive uncertainty, the pumped drinking water at the study site is most likely dominated by artificially‐infiltrated groundwater originating from the local infiltration canals. The model suggests that within the well field, the central pumping wells could be extracting regional groundwater, although the probability is relatively low. Nevertheless, a rigorous uncertainty assessment is still required since only a few realizations resulted in flow paths that support the field observations. Model results should therefore not be based on only one model realization; rather, an uncertainty analysis should be carried out to provide a sufficiently large suite of equally probable simulations that include all potential sources and pathways.

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“…A recent debates series (Rajaram, 2016;Fiori et al, 2016;Fogg and Zhang, 2016;Cirpka and Valocchi, 2016;Sanchez-Vila and Fernàndez-Garcia, 2016) outlined the gap between the advanced research in stochastic subsurface hydrology and its application in the practice of groundwater flow and transport modeling. We see a significant reason in the lack of data for complex stochastic models.…”

Section: Introductionmentioning

confidence: 99%

A Field Evidence Model: How to Predict Transport in a Heterogeneous Aquifers at Low Investigation Level?

Zech¹,

Dietrich²,

Atttinger³

et al. 2020

Preprint

Self Cite

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Aquifer heterogeneity in combination with data scarcity is a major challenge for reliable solute transport prediction. Velocity fluctuations cause non-regular plume shapes with potentially long tailing and/or fast travelling mass fractions.High monitoring cost and presumably missing simple concepts have limited the incorporation of heterogeneity to many field transport models up to now.We present a hierarchical aquifer model which combines large-scale deterministic structures and simple stochastic ap-5 proaches. Such a heterogeneous conductivity can easily be integrated into a numerical models. Depending on the modelling aim, the required structural complexity can be adapted. The same holds for the amount of available field data. The conductivity model is constructed step-wise following field evidence from observations; though relying on as minimal data as possible.Starting point are deterministic blocks, derived from head profiles and pumping tests. Then, sub-scale heterogeneity in form of random binary inclusions are introduced to each block. Structural parameters can be determined e.g. from flowmeter measure-10 ments.As proof of concept, we implemented a predictive transport model for the heterogeneous MADE site. The proposed hierarchical aquifer structure reproduces the plume development of the MADE-1 transport experiment without calibration. Thus, classical ADE models are able to describe highly skewed tracer plumes by incorporating deterministic contrasts and effects of connectivity in a stochastic way even without using uni-modal heterogeneity models with high variances. The reliance of 15 the conceptual model on few observations makes it appealing for a goal-oriented site specific transport analysis of less well investigated heterogeneous sites.

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Debates—Stochastic subsurface hydrology from theory to practice: The relevance of stochastic subsurface hydrology to practical problems of contaminant transport and remediation. What is characterization and stochastic theory good for?

Cited by 41 publications

References 36 publications

Can a Time Fractional‐Derivative Model Capture Scale‐Dependent Dispersion in Saturated Soils?

Can a Time Fractional‐Derivative Model Capture Scale‐Dependent Dispersion in Saturated Soils?

Pathline Density Distributions in a Null‐Space Monte Carlo Approach to Assess Groundwater Pathways

A Field Evidence Model: How to Predict Transport in a Heterogeneous Aquifers at Low Investigation Level?

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