Ground-water models cannot be validated

Konikow, Leonard F.; Bredehoeft, John

doi:10.1016/0309-1708(92)90033-x

Cited by 510 publications

(325 citation statements)

References 7 publications

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“…Groundwater-flow model inputs are nonunique, and multiple combinations of parameters exist that can provide similar fits to calibration targets (Anderson and others, 2015;Konikow and Bredehoeft, 1992). For example, if calibration targets were limited to groundwater levels, a unique, best-fit estimate of recharge and aquifer hydraulic conductivity could not be estimated because different combinations of recharge and aquifer hydraulic conductivity could produce the same groundwater level.…”

Section: Measurement Errors or Erroneous Data In 1940-2009mentioning

confidence: 99%

Groundwater-flow model of the northern High Plains aquifer in Colorado, Kansas, Nebraska, South Dakota, and Wyoming

Peterson¹,

Flynn²,

Traylor³

2016

Scientific Investigations Report

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Section: Measurement Errors or Erroneous Data In 1940-2009mentioning

confidence: 99%

Groundwater-flow model of the northern High Plains aquifer in Colorado, Kansas, Nebraska, South Dakota, and Wyoming

Peterson¹,

Flynn²,

Traylor³

2016

Scientific Investigations Report

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“…How quantitative models are parameterised and applied is only ever subjective (Oreskes and Belitz, 2001). Once developed in and for one site, it is often the case that models cannot travel to other contexts (Konikow and Bredehoeft, 1992). And, given that models are necessarily simplified representations, problems with the boundary conditions around prediction are often easily exacerbated and magnified the larger or more complex the model gets (Pilkey and Pilkey-Jarvis, 2007).…”

Section: Qualifying Quantificationmentioning

confidence: 99%

Social science perspectives on natural hazards risk and uncertainty

Jackson¹

2013

Risk and Uncertainty Assessment for Natural Hazards

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“…Second, obtaining an acceptable fit to the measurements of the dependent variables does not necessarily imply by itself the model's utility as a predictive tool or, equivalently, a correct model structure [Sun et al, 1998]. This point is illustrated with several case histories given by Konikow and Bredehoeft [1992]. Third, several model structures may fit the available data equally well by adjusting a number of their parameters through a calibration procedure.…”

Section: Model Error and Prior Informationmentioning

confidence: 99%

“…Second, they are used to predict the future response of a system under a set of new stresses. A number of authors [e.g., Beck, 1987;Binley et al, 1991;Konikow and Bredehoeft, 1992] suggest that mathematical models are better suited to the first use. It is argued that predictions regarding the response of a real system are often questionable and unreliable because of large uncertainties associated with errors in the model struc- ture and parameter error [Sun et al, 1998].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach to the quantification of the effect of model error on the predictions of groundwater models

Gaganis¹,

Smith²

2001

Water Resources Research

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Abstract. Errors arising from the imperfect mathematical representation of the structure of a hydrologic system (model error) are not random but systematic. Their effect on model predictions varies in space and time and differs for the flow and solute transport components of a groundwater model. Such errors do not necessarily have any probabilistic properties that can be easily exploited in the construction of a model performance criterion. A Bayesian approach is presented for quantifying model error in the presence of parameter uncertainty. Insight gained in updating the prior information on the model parameters is used to assess the correctness of the model structure, which is defined relative to the accuracy required of the model predictions. Model error is evaluated for each measurement of the dependent variable through an examination of the correctness of the model structure for different accuracy levels. The effect of model error on each dependent variable, which is quantified as a function of location and time, represents a measure of the reliability of the model in terms of each model prediction. This method can be used in identifying possible causes of model error and in discriminating among models in terms of the correctness of the model structure. It also offers an improved description of the uncertainties associated with a modeling exercise that may be useful in risk assessments and decision analyses. IntroductionA mathematical model is a set of equations or functions that contain adjustable parameters and constants. These functions or equations are mathematical descriptions of hypotheses regarding properties and processes of a more complex physical system. Groundwater models are always simplifications of reality, as we are unable to either characterize or describe mathematically the true complexity of a hydrologic system. We will refer to structural errors in a model, which can arise from incorrect hypotheses, unmodeled processes, or unknown correlations between processes, as model error. The parameters of a groundwater model, such as hydraulic conductivity, are always uncertain because of measurement error, heterogeneity, and scaling issues [Beckie, 1996]. The error produced from uncertainties in parameter values is called parameter error. In a stochastic modeling approach, a subset of the parameters is assigned error probability distributions to quantify the effects of parameter error. Error in the deterministically specified components of these probability distributions, such as in the form of the pdf, can also be considered a type of model error.Simulation models have two main uses in hydrogeology.

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Ground-water models cannot be validated

Cited by 510 publications

References 7 publications

Groundwater-flow model of the northern High Plains aquifer in Colorado, Kansas, Nebraska, South Dakota, and Wyoming

Groundwater-flow model of the northern High Plains aquifer in Colorado, Kansas, Nebraska, South Dakota, and Wyoming

Social science perspectives on natural hazards risk and uncertainty

A Bayesian Approach to the quantification of the effect of model error on the predictions of groundwater models

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