Using Markov Chain Monte Carlo to quantify parameter uncertainty and its effect on predictions of a groundwater flow model

Hassan, Ahmed E.; Bekhit, Hesham M.; Chapman, Jenny

doi:10.1016/j.envsoft.2008.11.002

Cited by 80 publications

(45 citation statements)

References 41 publications

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“…Furthermore, some properties of groundwater system cannot be measured directly, e.g. hydraulic conductivity and dispersivity, which are estimated indirectly by analyzing input and output measurements [2]. As the natural hydrological processes or groundwater movement is described by a group of simplified water flow governing equations, the predictions of groundwater system always deviate from observations.…”

Section: Conception Of the Uncertainty Of Groundwater Numerical Simulmentioning

confidence: 99%

“…In general, MCMC method is superior in strong flexibility, and high reliability in various environmental models' uncertainty analysis. Especially, MCMC method has a good performance on complex uncertainty issues which include high nonlinear, high dimensional and multimodal probability distribution [2,15]. Furthermore, MCMC method is inferior in huge computing time-consuming requirement.…”

Section: Uncertainty Analysis Of Groundwater Model Parametersmentioning

confidence: 99%

“…By constructing a Markov Chain with stable density distribution, the probability distribution space of target function is sufficiently searched in the process of evolving Markov Chain [2,15]. The searching process is constructed by two functions which are proposal and acceptance functions.…”

Section: Uncertainty Analysis Of Groundwater Model Parametersmentioning

confidence: 99%

“…As the natural hydrological processes or groundwater movement is described by a group of simplified water flow governing equations, the predictions of groundwater system always deviate from observations. Therefore, the uncertainty of groundwater simulation is inevitable, which is the main difficulty for describing groundwater flow and solute transport activities accurately [2,3].…”

Section: Conception Of the Uncertainty Of Groundwater Numerical Simulmentioning

confidence: 99%

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Review of the uncertainty analysis of groundwater numerical simulation

Zeng

2013

Chin. Sci. Bull.

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Groundwater system is a complex and open system, which is affected by natural conditions and human activities. Natural hydrological processes is conceptualized through relatively simple flow governing equations in groundwater models. Moreover, observation data is always limited in field hydrogeological conditions. Therefore, the predictive results of groundwater simulation often deviate from true values, which is attribute to the uncertainty of groundwater numerical simulation. According to the process of system simulation, the uncertainty sources of groundwater numerical simulation can be divided into model parameters, conceptual model and observation data uncertainties. In addition, the uncertainty stemmed from boundary conditions is sometimes refered as scenario uncertainty. In this paper, the origination and category of groundwater modeling uncertainty are analyzed. The recent progresses on the methods of groundwater modeling uncertainty analysis are reivewed. Furthermore, the researches on the comprehensive analysis of uncertainty sources, and the predictive uncertainty of model outputs are discussed. Finally, several prospects on the deveolpment of groundwater modeling uncetainty analysis are proposed.groundwater modeling, uncertainty analysis, model parameter, conceptual model, observation data Citation:

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Section: Conception Of the Uncertainty Of Groundwater Numerical Simulmentioning

confidence: 99%

Section: Uncertainty Analysis Of Groundwater Model Parametersmentioning

confidence: 99%

Section: Uncertainty Analysis Of Groundwater Model Parametersmentioning

confidence: 99%

Section: Conception Of the Uncertainty Of Groundwater Numerical Simulmentioning

confidence: 99%

See 2 more Smart Citations

Review of the uncertainty analysis of groundwater numerical simulation

Zeng

2013

Chin. Sci. Bull.

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“…To characterize the spatial variability of the hydraulic conductivity, random space function (RSF) is often adopted (Ezzedine and Rubin 1996;Feyen et al 2003;Franssen et al 2003;Kerrou et al 2008;Hassan et al 2009;Liang et al 2009Liang et al , 2010. However, scarcity of measurements leads to generating spatial structure pattern of hydraulic conductivity at a few sampling location, which arouse uncertainty of spatial hydraulic conductivity distribution.…”

Section: Introductionmentioning

confidence: 99%

Bayesian approach to quantify parameter uncertainty and impacts on predictive flow and mass transport in heterogeneous aquifer

Liang

Zeng

Shen

et al. 2013

Int. J. Environ. Sci. Technol.

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Groundwater flow and mass transport predictions are subjected to uncertainty due to heterogeneity of hydraulic conductivity, whose variability in space is considerably higher than that of other hydraulic properties relevant to groundwater flow. To characterize the distribution of hydraulic conductivity, random space function (RSF) is often used. The Bayesian approach was applied to quantitatively study the effect of parameter uncertainty in RSF on a hypothetical two-dimensional uniform groundwater flow and mass transport. Specifically, the parameter uncertainty transmitted to macrodispersion in mass transport model was also inferred. The results showed that the posterior probability distributions of parameters were updated after Bayesian inference. The numerical experiments indicated that the overall predictive uncertainty was increased with simulating time along the flow direction. As to the relative contribution of the two types of uncertainty, it indicated that parametric uncertainty was a little more important than stochastic uncertainty for the predictive uncertainty of hydraulic head. When the uncertainty of hydraulic head as well as macrodispersion was transported to mass transport model, a much bigger contribution of stochastic uncertainty was observed. Therefore, parametric uncertainty should not be neglected during the process of subsurface simulation.

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The influence of the length of the calibration period and observation frequency on predictive uncertainty in time series modeling of groundwater dynamics

Spek

Bakker

2017

Water Resources Research

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The influence of the length of the calibration period and observation frequency on the predictive uncertainty in time series modeling of groundwater dynamics is investigated. Studied series are from deltaic regions with predominantly shallow groundwater tables in a temperate maritime climate where heads vary due to precipitation and evaporation. Response times vary over a wide range from ∼60 to ∼1200 days. A Transfer Function‐Noise model is calibrated with the Markov Chain Monte Carlo method to both synthetic series and measured series of heads. The model fit and uncertainty are evaluated for various calibration periods and observation frequencies. It is often assumed that the required length of the calibration period is related to the response time of the system. In this study, no strong relationship was observed. Results indicate, however, that the required length of the calibration period is related to the decay time of the noise. Furthermore, the length of the calibration period was much more important than the total number of observations. For the measured series, the credible intervals could commonly be reduced to ∼10% of the measured head range and the prediction intervals to ∼50% of the measured head range with calibration periods of 20 years with approximately two observations per month.

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Using Markov Chain Monte Carlo to quantify parameter uncertainty and its effect on predictions of a groundwater flow model

Cited by 80 publications

References 41 publications

Review of the uncertainty analysis of groundwater numerical simulation

Review of the uncertainty analysis of groundwater numerical simulation

Bayesian approach to quantify parameter uncertainty and impacts on predictive flow and mass transport in heterogeneous aquifer

The influence of the length of the calibration period and observation frequency on predictive uncertainty in time series modeling of groundwater dynamics

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