Uncertainty In Groundwater Modeling
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Uncertainty In Groundwater Modeling
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Author :
language : en
Publisher:
Release Date : 2017
Uncertainty In Groundwater Modeling written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Groundwater categories.
Calibration And Reliability In Groundwater Modelling
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Author : Marc F. P. Bierkens
language : en
Publisher:
Release Date : 2006
Calibration And Reliability In Groundwater Modelling written by Marc F. P. Bierkens and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Science categories.
Several of the papers here deal with decision making under uncertainty.
Effective Groundwater Model Calibration
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Author : Mary C. Hill
language : en
Publisher: John Wiley & Sons
Release Date : 2006-08-25
Effective Groundwater Model Calibration written by Mary C. Hill and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-08-25 with Technology & Engineering categories.
Methods and guidelines for developing and using mathematical models Turn to Effective Groundwater Model Calibration for a set of methods and guidelines that can help produce more accurate and transparent mathematical models. The models can represent groundwater flow and transport and other natural and engineered systems. Use this book and its extensive exercises to learn methods to fully exploit the data on hand, maximize the model's potential, and troubleshoot any problems that arise. Use the methods to perform: Sensitivity analysis to evaluate the information content of data Data assessment to identify (a) existing measurements that dominate model development and predictions and (b) potential measurements likely to improve the reliability of predictions Calibration to develop models that are consistent with the data in an optimal manner Uncertainty evaluation to quantify and communicate errors in simulated results that are often used to make important societal decisions Most of the methods are based on linear and nonlinear regression theory. Fourteen guidelines show the reader how to use the methods advantageously in practical situations. Exercises focus on a groundwater flow system and management problem, enabling readers to apply all the methods presented in the text. The exercises can be completed using the material provided in the book, or as hands-on computer exercises using instructions and files available on the text's accompanying Web site. Throughout the book, the authors stress the need for valid statistical concepts and easily understood presentation methods required to achieve well-tested, transparent models. Most of the examples and all of the exercises focus on simulating groundwater systems; other examples come from surface-water hydrology and geophysics. The methods and guidelines in the text are broadly applicable and can be used by students, researchers, and engineers to simulate many kinds systems.
Applied Groundwater Modeling
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Author : Mary P. Anderson
language : en
Publisher: Academic Press
Release Date : 2015-08-13
Applied Groundwater Modeling written by Mary P. Anderson and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-08-13 with Technology & Engineering categories.
This second edition is extensively revised throughout with expanded discussion of modeling fundamentals and coverage of advances in model calibration and uncertainty analysis that are revolutionizing the science of groundwater modeling. The text is intended for undergraduate and graduate level courses in applied groundwater modeling and as a comprehensive reference for environmental consultants and scientists/engineers in industry and governmental agencies. Explains how to formulate a conceptual model of a groundwater system and translate it into a numerical model Demonstrates how modeling concepts, including boundary conditions, are implemented in two groundwater flow codes-- MODFLOW (for finite differences) and FEFLOW (for finite elements) Discusses particle tracking methods and codes for flowpath analysis and advective transport of contaminants Summarizes parameter estimation and uncertainty analysis approaches using the code PEST to illustrate how concepts are implemented Discusses modeling ethics and preparation of the modeling report Includes Boxes that amplify and supplement topics covered in the text Each chapter presents lists of common modeling errors and problem sets that illustrate concepts
Assessment Of Parametric And Model Uncertainty In Groundwater Modeling
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Author : Dan Lu
language : en
Publisher:
Release Date : 2012
Assessment Of Parametric And Model Uncertainty In Groundwater Modeling written by Dan Lu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Engineering categories.
ABSTRACT: Groundwater systems are open and complex, rendering them prone to multiple conceptual interpretations and mathematical descriptions. When multiple models are acceptable based on available knowledge and data, model uncertainty arises. One way to assess the model uncertainty is postulating several alternative hydrologic models for a site and using model selection criteria to (1) rank these models, (2) eliminate some of them, and/or (3) weight and average predictions statistics generated by multiple models based on their model probabilities. This multimodel analysis has led to some debate among hydrogeologists about the merits and demerits of common model selection criteria such as AIC, AICc, BIC, and KIC. This dissertation contributes to the discussion by comparing the abilities of the two common Bayesian criteria (BIC and KIC) theoretically and numerically. The comparison results indicate that, using MCMC results as a reference, KIC yields more accurate approximations of model probability than does BIC. Although KIC reduces asymptotically to BIC, KIC provides consistently more reliable indications of model quality for a range of sample sizes. In the multimodel analysis, the model averaging predictive uncertainty is a weighted average of predictive uncertainties of individual models. So it is important to properly quantify individual model's predictive uncertainty. Confidence intervals based on regression theories and credible intervals based on Bayesian theories are conceptually different ways to quantify predictive uncertainties, and both are widely used in groundwater modeling. This dissertation explores their differences and similarities theoretically and numerically. The comparison results indicate that given Gaussian distributed observation errors, for linear or linearized nonlinear models, linear confidence and credible intervals are numerically identical when consistent prior parameter information is used. For nonlinear models, nonlinear confidence and credible intervals can be numerically identical if parameter confidence and credible regions based on approximate likelihood method are used and intrinsic model nonlinearity is small; but they differ in practice due to numerical difficulties in calculating both confidence and credible intervals. Model error is a more vital issue than differences between confidence and credible intervals for individual models, suggesting the importance of considering alternative models. Model calibration results are the basis for the model selection criteria to discriminate between models. However, how to incorporate calibration data errors into the calibration process is an unsettled problem. It has been seen that due to the improper use of the error probability structure in the calibration, the model selection criteria lead to an unrealistic situation in which one model receives overwhelmingly high averaging weight (even 100%), which cannot be justified by available data and knowledge. This dissertation finds that the errors reflected in the calibration should include two parts, measurement errors and model errors. To consider the probability structure of the total errors, I propose an iterative calibration method with two stages of parameter estimation. The multimodel analysis based on the estimation results leads to more reasonable averaging weights and better averaging predictive performance, compared to those with considering only measurement errors. Traditionally, data-worth analyses have relied on a single conceptual-mathematical model with prescribed parameters. Yet this renders model predictions prone to statistical bias and underestimation of uncertainty and thus affects the groundwater management decision. This dissertation proposes a multimodel approach to optimum data-worth analyses that is based on model averaging within a Bayesian framework. The developed multimodel Bayesian approach to data-worth analysis works well in a real geostatistical problem. In particular, the selection of target for additional data collection based on the approach is validated against actual data collected. The last part of the dissertation presents an efficient method of Bayesian uncertainty analysis. While Bayesian analysis is vital to quantify predictive uncertainty in groundwater modeling, its application has been hindered in multimodel uncertainty analysis because of computational cost of numerous models executions and the difficulty in sampling from the complicated posterior probability density functions of model parameters. This dissertation develops a new method to improve computational efficiency of Bayesian uncertainty analysis using sparse-grid method. The developed sparse-grid-based method for Bayesian uncertainty analysis demonstrates its superior accuracy and efficiency to classic importance sampling and MCMC sampler when applied to a groundwater flow model.
Calibration And Reliability In Groundwater Modelling
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Author : Fritz Stauffer
language : en
Publisher:
Release Date : 2000
Calibration And Reliability In Groundwater Modelling written by Fritz Stauffer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Groundwater categories.
Groundwater Quality Modeling And Management Under Uncertainty
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Author : Srikanta Mishra
language : en
Publisher:
Release Date : 2003
Groundwater Quality Modeling And Management Under Uncertainty written by Srikanta Mishra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Psychology categories.
This collection contains 30 peer-reviewed papers presented at a symposium, Probabilistic Approaches and Groundwater Modeling, at the 2003 World Environmental and Water Resources Congress, held in Philadelphia, Pennsylvania, June 24-26, 2003.
Information Theory Approach To Quantifying Parameter Uncertainty In Groundwater Modeling
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Author : Alston Marian Noronha
language : en
Publisher:
Release Date : 2005
Information Theory Approach To Quantifying Parameter Uncertainty In Groundwater Modeling written by Alston Marian Noronha and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
The complexity of geologic and hydrologic subsurface structures often makes it difficult to formulate an accurate groundwater model. To improve the accuracy of a model, many studies have focused on optimizing unknown parameters without considering their uncertainty. The Information Theory (IT) approach uses entropy as a measure of uncertainty for the most probable state of a system. We maximize entropy when the groundwater model is optimized, by imposing normalization constraint and error constraint with observation data. A three-dimensional synthetic model is simulated using MODFLOW-2000 to demonstrate the effectiveness of the IT approach. With hydraulic heads as the observed data, hydraulic conductivities are chosen as the parameters to be optimized. The IT approach calculates variance, covariance and correlation coefficients for multiple unknown parameters. The Kansas City Plant case study shows that the IT approach is able to direct site exploration to detect the unknown structure that may affect the remediation performance.
Use Of Numerical Groundwater Modeling To Evaluate Uncertainty In Conceptual Models Of Recharge And Hydrostratigraphy
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Author :
language : en
Publisher:
Release Date : 2007
Use Of Numerical Groundwater Modeling To Evaluate Uncertainty In Conceptual Models Of Recharge And Hydrostratigraphy written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
Numerical groundwater models are based on conceptualizations of hydrogeologic systems that are by necessity developed from limited information and therefore are simplifications of real conditions. Each aspect (e.g. recharge, hydrostratigraphy, boundary conditions) of the groundwater model is often based on a single conceptual model that is considered to be the best representation given the available data. However, the very nature of their construction means that each conceptual model is inherently uncertain and the available information may be insufficient to refute plausible alternatives, thereby raising the possibility that the flow model is underestimating overall uncertainty. In this study we use the Death Valley Regional Flow System model developed by the U.S. Geological Survey as a framework to predict regional groundwater flow southward into Yucca Flat on the Nevada Test Site. An important aspect of our work is to evaluate the uncertainty associated with multiple conceptual models of groundwater recharge and subsurface hydrostratigraphy and quantify the impacts of this uncertainty on model predictions. In our study, conceptual model uncertainty arises from two sources: (1) alternative interpretations of the hydrostratigraphy in the northern portion of Yucca Flat where, owing to sparse data, the hydrogeologic system can be conceptualized in different ways, and (2) uncertainty in groundwater recharge in the region as evidenced by the existence of several independent approaches for estimating this aspect of the hydrologic system. The composite prediction of groundwater flow is derived from the regional model that formally incorporates the uncertainty in these alternative input models using the maximum likelihood Bayesian model averaging method. An assessment of the joint predictive uncertainty of the input conceptual models is also produced. During this process, predictions of the alternative models are weighted by model probability, which is the degree of belief that a model is more plausible given available prior information (expert opinion) and site measurements (hydraulic head and groundwater flux). The results indicate that flow simulations in Yucca Flat are more sensitive to hydrostratigraphic model than recharge model. Furthermore, posterior model uncertainty is dominated by inter-model variance as opposed to intra-model variance, indicating that conceptual model uncertainty has greater impact on the results than parametric uncertainty. Without consideration of conceptual model uncertainty, uncertainty in the flow predictions would be significantly underestimated. Incorporation of the uncertainty in multiple conceptual models renders the groundwater flow model predictions more scientifically defensible.
Parameter Estimation And Uncertainty Quantification In Water Resources Modeling
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Author : Philippe Renard
language : en
Publisher: Frontiers Media SA
Release Date : 2020-04-22
Parameter Estimation And Uncertainty Quantification In Water Resources Modeling written by Philippe Renard and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-22 with categories.
Numerical models of flow and transport processes are heavily employed in the fields of surface, soil, and groundwater hydrology. They are used to interpret field observations, analyze complex and coupled processes, or to support decision making related to large societal issues such as the water-energy nexus or sustainable water management and food production. Parameter estimation and uncertainty quantification are two key features of modern science-based predictions. When applied to water resources, these tasks must cope with many degrees of freedom and large datasets. Both are challenging and require novel theoretical and computational approaches to handle complex models with large number of unknown parameters.