Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty

Meyer, Philip D.; Ye, Ming; Neuman, Shlomo P.; Cantrell, Kirk J.

doi:10.2172/974518

Cited by 16 publications

(13 citation statements)

References 50 publications

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“…As discussed in Ye et al (2004) and Meyer et al (2004), basing the analysis on a set of model alternatives that do not encompass all possibilities implies a relative comparison between models. We thus interpret prior model probabilities to be subjective values reflecting a belief about the relative plausibility of each model based on its apparent (qualitative, a priori) consistency with available knowledge and data.…”

Section: Bayesian Model Averagingcombining Conceptual Model and Parammentioning

confidence: 99%

See 1 more Smart Citation

Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty with Application to Uranium Transport at the Hanford Site 300 Area

Meyer¹,

Ye²,

Rockhold³

et al. 2007

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Section: Bayesian Model Averagingcombining Conceptual Model and Parammentioning

confidence: 99%

“…This may be computationally and hydrologically very demanding. MLBMA (Neuman 2003;Ye et al 2004;Meyer et al 2004) uses two approximations to simplify the application of BMA in hydrogeological modeling. First, approximating ( )…”

Section: Maximum Likelihood Bayesian Model Averaging (Mlbma)mentioning

confidence: 99%

Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty with Application to Uranium Transport at the Hanford Site 300 Area

Meyer¹,

Ye²,

Rockhold³

et al. 2007

Self Cite

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“…The debate as to whether or not postulate simplified or complex/elaborated models to explain a groundwater system [see, e.g., Neuman and Wierenga , 2003; Gómez‐Hernández , 2006; Hill , 2006; Hill and Tiedeman , 2007; Hunt et al , 2007; Renard , 2007], the advances in computational power, as well as the increasing awareness among scientists to address uncertainty in model predictions [see, e.g., Walker and Marchau , 2003; Refsgaard et al , 2005; Van der Sluijs , 2005; Pappenberger and Beven , 2006; Refsgaard et al , 2007] have stimulated a growing tendency of postulating alternative conceptualizations [e.g., Harrar et al , 2003; Meyer et al , 2004; Højberg and Refsgaard , 2005; Meyer et al , 2007; Troldborg et al , 2007; Rojas et al , 2008; Seifert et al , 2008; Ijiri et al , 2009; Rojas et al , 2010; R. Rojas et al, Application of a multimodel approach to account for conceptual model and scenario uncertainties in groundwater modelling, submitted to Journal of Hydrology , 2009]. Rather than relying on a single conceptual model, it seems more appropriate to consider a range of plausible system representations and analyze the combined multimodel output to assess the predictive modeling uncertainty.…”

Section: Introduction and Scopementioning

confidence: 99%

On the value of conditioning data to reduce conceptual model uncertainty in groundwater modeling

Rojas

Feyen

Batelaan

et al. 2010

Water Resources Research

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[1] Recent applications of multimodel methods have demonstrated their potential in quantifying conceptual model uncertainty in groundwater modeling applications. To date, however, little is known about the value of conditioning to constrain the ensemble of conceptualizations, to differentiate among retained alternative conceptualizations, and to reduce conceptual model uncertainty. We address these questions by conditioning multimodel simulations on measurements of hydraulic conductivity and observations of system-state variables and evaluating the effects on (1) the posterior multimodel statistics and (2) the contribution of conceptual model uncertainty to the predictive uncertainty. Multimodel aggregation and conditioning is performed by combining the Generalized Likelihood Uncertainty Estimation (GLUE) method and Bayesian Model Averaging (BMA). As an illustrative example we employ a 3-dimensional hypothetical system under steady state conditions, for which uncertainty about the conceptualization is expressed by an ensemble (M) of seven models with varying complexity. Results show that conditioning on heads allowed for the exclusion of the two simplest models, but that their information content is limited to further differentiate among the retained conceptualizations. Conditioning on increasing numbers of conductivity measurements allowed for a further refinement of the ensemble M and resulted in an increased precision and accuracy of the multimodel predictions. For some groundwater flow components not included as conditioning data, however, the gain in accuracy and precision was partially offset by strongly deviating predictions of a single conceptualization. Identifying the conceptualization producing the most deviating predictions may guide data collection campaigns aimed at acquiring data to further eliminate such conceptualizations. Including groundwater flow and river discharge observations further allowed for a better differentiation among alternative conceptualizations and drastic reductions of the predictive variances. Results strongly advocate the use of observations less commonly available than groundwater heads to reduce conceptual model uncertainty in groundwater modeling.Citation: Rojas, R., L. Feyen, O. Batelaan, and A. Dassargues (2010), On the value of conditioning data to reduce conceptual model uncertainty in groundwater modeling, Water Resour. Res., 46, W08520,

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“…The uncertainty associated with the sorption parameter Kd can be placed into three major categories (Meyer et al, 2004): i) Experimental uncertainty (errors due to measurements), ii) Sorption process chemistry uncertainty (variation in solution chemistry, i.e. complexation, competitive adsorption and alteration of the adsorption-site chemistry; variation in surface adsorption sites, i.e.…”

Section: P Van Der Keur and B V Iversen: Soil Physical Uncertaintymentioning

confidence: 99%

“…Meyer et al (1997) resampled the distributions and derived closedform distributions of the soil hydraulic parameters that can be used to represent parameter uncertainty when the information about a soil is limited to its textural class (Meyer et al, 1997). Meyer et al (2004) listed derived dry bulk density, compiled by from the U.S. Natural Resources Conservation Service Soil Database (NRC-SSC) divided according to the USDA soil textural class. For each textural class, the Kolmogorov-Smirnov D-statistic was calculated using hypothetical normal and lognormal distributions.…”

Section: Uncertainty Related To Derivation Of Soil Hydraulic Propertimentioning

confidence: 99%

Uncertainty in soil physical data at river basin scale – a review

Keur¹,

Iversen²

2006

Hydrol. Earth Syst. Sci.

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Abstract.For hydrological modelling studies at the river basin scale, decision makers need guidance in assessing the implications of uncertain data used by modellers as an input to modelling tools. Simulated solute transport through the unsaturated zone is associated with uncertainty due to spatial variability of soil hydraulic properties and derived hydraulic model parameters. In general for modelling studies at the river basin scale spatially available data at various scales must be aggregated to an appropriate scale. Estimating soil properties at unsampled points by means of geostatistical techniques require reliable information on the spatial structure of soil data. In this paper this information is assessed by reviewing current developments in the field of soil physical data uncertainty and adopting a classification system. Then spatial variability and structure is inspected by reviewing experimental work on determining spatial length scales for soil physical (and soil chemical) data. Available literature on spatial length scales for soil physical-and chemical properties is reviewed and their use in facilitating change of spatial support discussed. Uncertainty associated to the derivation of hydraulic properties from soil physical properties in this context is also discussed.

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Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty

Cited by 16 publications

References 50 publications

Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty with Application to Uranium Transport at the Hanford Site 300 Area

Combined Estimation of Hydrogeologic Conceptual Model, Parameter, and Scenario Uncertainty with Application to Uranium Transport at the Hanford Site 300 Area

On the value of conditioning data to reduce conceptual model uncertainty in groundwater modeling

Uncertainty in soil physical data at river basin scale – a review

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