Bayesian Markov-chain-Monte-Carlo inversion of time-lapse cross hole ground-penetrating radar data to characterize the vadose zone at the Arrenaes field site, Denmark

The ground-penetrating radar (GPR) geophysical method has the potential to provide valuable information on the hydraulic properties of the vadose zone because of its strong sensitivity to soil water content. In particular, recent evidence has suggested that the stochastic inversion of crosshole GPR traveltime data can allow for a significant reduction in uncertainty regarding subsurface van Genuchten-Mualem (VGM) parameters. Much of the previous work on the stochastic estimation of VGM parameters from crosshole GPR data has considered the case of steady-state infiltration conditions, which represent only a small fraction of practically relevant scenarios. We explored in detail the dynamic infiltration case, specifically examining to what extent time-lapse crosshole GPR traveltimes, measured during a forced infiltration experiment at the Arreneas field site in Denmark, could help to quantify VGM parameters and their uncertainties in a layered medium, as well as the corresponding soil hydraulic properties. We used a Bayesian Markov-chain-Monte-Carlo inversion approach. We first explored the advantages and limitations of this approach with regard to a realistic synthetic example before applying it to field measurements. In our analysis, we also considered different degrees of prior information. Our findings indicate that the stochastic inversion of the time-lapse GPR data does indeed allow for a substantial refinement in the inferred posterior VGM parameter distributions compared with the corresponding priors, which in turn significantly improves knowledge of soil hydraulic properties. Overall, the results obtained clearly demonstrate the value of the information contained in time-lapse GPR data for characterizing vadose zone dynamics

Confronting input, parameter, structural, and measurement uncertainty in multi-site multiple-response watershed modeling using Bayesian inferences

2012 Fall.Includes bibliographical references.Simulation modeling is arguably one of the most powerful scientific tools available to address questions, assess alternatives, and support decision making for environmental management. Watershed models are used to describe and understand hydrologic and water quality responses of land and water systems under prevailing and projected conditions. Since the promulgation of the Clean Water Act of 1972 in the United States, models are increasingly used to evaluate potential impacts of mitigation strategies and support policy instruments for pollution control such as the Total Maximum Daily Load (TMDL) program. Generation, fate, and transport of water and contaminants within watershed systems comprise a highly complex network of interactions. It is difficult, if not impossible, to capture all important processes within a modeling framework. Although critical natural processes and management actions can be resolved at varying spatial and temporal scales, simulation models will always remain an approximation of the real system. As a result, the use of models with limited knowledge of the system and model structure is fraught with uncertainty. Wresting environmental decisions from model applications must consider factors that could conspire against credible model outcomes. The main goal of this study is to develop a novel Bayesian-based computational framework for characterization and incorporation of uncertainties from forcing inputs, model parameters, model structures, and measured responses in the parameter estimation process for multisite multiple-response watershed modeling. Specifically, the following objectives are defined: (i) to evaluate the effectiveness and efficiency of different computational strategies in sampling the model parameter space; (ii) to examine the role of measured responses at various locations in the stream network as well as intra-watershed processes in enhancing the model performance credibility; (iii) to facilitate combining predictions from competing model structures; and (iv) to develop a statistically rigorous procedure for incorporation of errors from input, parameter, structural and measurement sources in the parameter estimation process. The proposed framework was applied for simulating streamflow and total nitrogen at multiple locations within a 248 square kilometer watershed in the Midwestern United States using the Soil and Water Assessment Tool (SWAT). Results underlined the importance of simultaneous treatment of all sources of uncertainty for parameter estimation. In particular, it became evident that incorporation of input uncertainties was critical for determination of model structure for runoff generation and also representation of intra-watershed processes such as denitrification rate and dominant pathways for transport of nitrate within the system. The computational framework developed in this study can be implemented to establish credibility for modeling watershed processes. More importantly, the framework can reveal how collection of data from different responses at different locations within a watershed system of interest would enhance the predictive capability of watershed models by reducing input, parametric, structural, and measurement uncertainties

Model-data interaction in groundwater studies: Review of methods, applications and future directions

This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ which permits use, distribution and reproduction in any medium, provided the original work is properly cited. This author accepted manuscript is made available following 24 month embargo from date of publication (Sept 2018) in accordance with the publisher’s archiving policyWe define model-data interaction (MDI) as a two way process between models and data, in which on one hand data can serve the modeling purpose by supporting model discrimination, parameter refinement, uncertainty analysis, etc., and on the other hand models provide a tool for data fusion, interpretation, interpolation, etc. MDI has many applications in the realm of groundwater and has been the topic of extensive research in the groundwater community for the past several decades. This has led to the development of a multitude of increasingly sophisticated methods. The progress of data acquisition technologies and the evolution of models are continuously changing the landscape of groundwater MDI, creating new challenges and opportunities that must be properly understood and addressed. This paper aims to review, analyze and classify research on MDI in groundwater applications, and discusses several related aspects including: (1) basic theoretical concepts and classification of methods, (2) sources of uncertainty and how they are commonly addressed, (3) specific characteristics of groundwater models and data that affect the choice of methods, (4) how models and data can interact to provide added value in groundwater applications, (5) software and codes for MDI, and (6) key issues that will likely form future research directions. The review shows that there are many tools and techniques for groundwater MDI, and this diversity is needed to support different MDI objectives, assumptions, model and data types and computational constraints. The study identifies eight categories of applications for MDI in the groundwater literature, and highlights the growing gap between MDI practices in the research community and those in consulting, industry and government.Behzad Ataie-Ashtiani and Craig T. Simmons acknowledge support from the National Centre for Groundwater Research and Training, Australia. Behzad Ataie-Ashtiani also appreciates the support of the Research Office of the Sharif University of Technology, Iran