Procedures for Uncertainty Analyses Applied to a Landfill Leachate Plume

Abbaspour, Karim C.; Schulin, Rainer; Genuchten, M. Th. van; Schläppi, E.

doi:10.1111/j.1745-6584.1998.tb02094.x

Cited by 11 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research on the effect of this model and parameter uncertainty on the prediction of hydrological variables or derivatives like a well capture zone is rather limited. In an application of their Bayesian uncertainty development algorithm to a landfill analysis, Abbaspour et al [1998] invoked a geostatistical model with uncertain parameters to model the hydraulic conductivity. The uncertainty in the parameters was propagated to a goal function using a two-dimensional transport model.…”

Section: Spatial Stochastic Approach and Uncertaintymentioning

confidence: 99%

Stochastic capture zone delineation within the generalized likelihood uncertainty estimation methodology: Conditioning on head observations

Feyen¹,

Beven²,

Smedt³

et al. 2001

Water Resources Research

View full text Add to dashboard Cite

Abstract. A stochastic methodology to evaluate the predictive uncertainty in well capture zones in heterogeneous aquifers with uncertain parameters is presented. The approach is based on the generalized likelihood uncertainty estimation methodology. The hydraulic conductivity is modeled as a random space function allowing for the uncertainty that stems from the imperfect knowledge of the parameters of the correlation structure. Parameters are sampled from prior distributions and are used for the generation of a large number of hydraulic conductivity fields, which are subsequently used to solve the groundwater flow equation. A likelihood is calculated for every simulation, based on some goodness-of-fit measure between simulated heads and available observations. Using inverse particle tracking, a capture zone is determined which is assigned the likelihood calculated for that particular simulation. Statistical analysis of the ensemble of all simulations enables the predictive uncertainty of the well capture zones to be defined. Results are presented for a hypothetical test case and different likelihood definitions used in the conditioning process. The results show that the delineated capture zones are most sensitive to the mean hydraulic conductivity and the variance, whereas the integral scale of the variogram is the parameter with the smallest influence. For all likelihood measures the prior uncertainty is reduced considerably by introducing the observation heads, but the reduction is most effective for the very selective likelihood definition. The method presented can be used in real applications to quantify the uncertainty in the location and extent of well capture zones when little or no information is available about the hydraulic properties, through the conditioning on head observations.

show abstract

Section: Spatial Stochastic Approach and Uncertaintymentioning

confidence: 99%

Stochastic capture zone delineation within the generalized likelihood uncertainty estimation methodology: Conditioning on head observations

Feyen¹,

Beven²,

Smedt³

et al. 2001

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…Discontinuous unpublished leachate discharge datasets were made available by the city authorities and could be integrated to the water balance. In Phase III, this model was tested by Abbaspour et al (1999) with an algorithm of Bayesian statistics (BUDA; see Abbaspour et al 1998). With the available data for hydraulic conductivity, aquifer geometry Fig.…”

Section: Water Balance and Groundwater Flow Modelmentioning

confidence: 99%

Investigative strategies and risk assessment of old unlined municipal solid waste landfills

et al. 2000

View full text Add to dashboard Cite

A case study was carried out at an old landfill site (Riet site) located in Winterthur, northern Switzerland. The Riet site contains mainly municipal solid wastes (MSW) comprising four discrete unlined landfill compartments, the oldest of which dates back to 1918. One goal of the case study was to assess the investigative techniques used to determine the risk of groundwater contamination. Waste compartments and leachate plumes were delineated with the electromagnetic method EM31 and with tracer methods. The most efficient methods for a risk assessment were flow modelling based on reliable hydraulic head and hydraulic conductivity measurements, and chemical analyses. Chemical analyses of leachate and groundwater samples showed that the older compartments of the landfill pose only a small risk to the groundwater. The other goal of the study was to determine the long-term risk of groundwater contamination posed by MSW landfills of different ages. To this end, chemical concentrations of leachate from the Riet site were compared with those of other MSW landfills of various ages. The longterm risk of landfill compartments older than about 1960 can be considered in general to be considerably smaller than that of younger ones. Age of the compartment and leachate concentrations could be correlated.

show abstract

“…Bayesian statistical methods for history matching were used in a study of remediation of a groundwater contamination site [1,2]. The reference that comes closest to our point of view is [53].…”

Section: Flow In Porous Mediamentioning

confidence: 99%

“…They are thus important in the practical implementation of the Bayesian approach, in the evaluation of (2), and in the determination of the maximum likelihood m relative to the posterior distribution (1).…”

mentioning

confidence: 99%

Stochastic methods for the prediction of complex multiscale phenomena

Glimm¹,

Sharp²

1998

Quart. Appl. Math.

View full text Add to dashboard Cite

Abstract.The purpose of this paper is to develop a general framework for the prediction of complex multiscale phenomena and to illustrate this framework through comparison to two examples of current interest to the authors.Prediction involves a two-step process of inverse prediction to describe the system, given observations of its behavior, and forward prediction, to specify system behavior, given its description. Introduction.This paper is concerned with methods for the analysis and prediction of complex phenomena. The complexity of systems considered here results from the interaction of a number of subsystems, or within a single subsystem from the interaction of phenomena occurring on a number of length and/or time scales.The analysis and prediction problem divides into two components, the forward problem and the inverse problem. The forward problem is to determine the solution describing system behavior, given the governing equations and initial data. The inverse problem is to determine the governing equations and initial data, given observations of the system. These two problems are linked in usage as the inverse problem, on the basis of current observations, and give the governing equations and data needed to solve the forward problem, which in turn makes predictions going beyond current observations. Forward prediction is very different from inverse prediction by statistical inference. Forward prediction assumes complete data and gives a complete answer. Even in the stochastic case, the data and the answer for forward prediction are complete within a probabilistic framework. For statistical inference, to solve the inverse prediction problem,

show abstract

Procedures for Uncertainty Analyses Applied to a Landfill Leachate Plume

Cited by 11 publications

References 22 publications

Stochastic capture zone delineation within the generalized likelihood uncertainty estimation methodology: Conditioning on head observations

Stochastic capture zone delineation within the generalized likelihood uncertainty estimation methodology: Conditioning on head observations

Investigative strategies and risk assessment of old unlined municipal solid waste landfills

Stochastic methods for the prediction of complex multiscale phenomena

Contact Info

Product

Resources

About