Anneli Guthke (geb. Schöniger)

Anneli Guthke (geb. Schöniger)
Universität Stuttgart · Institute for Modelling Hydraulic and Environmental Systems

20.79
 · 
Dr. rer. nat. Dipl.-Ing.

About

38
Publications
6,681
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349
Citations
Research Experience
January 2017 - present
Universität Stuttgart
Position
  • Risk and uncertainty assessment
Description
  • Postdoc position within the LS3 group headed by Prof. Wolfgang Nowak
September 2015 - December 2016
University of Tuebingen
Position
  • Bayesian assessment of conceptual uncertainty in hydrosystem modeling
Description
  • Postdoc position within the hydrogeology group headed by Prof. Olaf A. Cirpka (University of Tübingen), in collaboration with the stochastic simulation & safety research for hydrosystems group headed by Prof. Wolfgang Nowak (University of Stuttgart)
June 2012 - August 2015
University of Tuebingen
Position
  • Bayesian assessment of conceptual uncertainty in hydrosystem modeling
Description
  • PhD thesis work with Prof. Wolfgang Nowak, Dr. Thomas Wöhling, Prof. Walter A. Illman, and Prof. Olaf A. Cirpka
Education
June 2012 - August 2015
University of Tuebingen
Field of study
  • Integrated Hydrosystem Modelling
October 2004 - March 2010
Universität Stuttgart
Field of study
  • Environmental Engineering

Publications

Publications (38)
Article
This article provides a structured discussion of defensible model complexity in view of modeling goals and available data.
Article
Full-text available
Bayesian model selection or averaging objectively ranks a number of plausible, competing conceptual models based on Bayes' theorem. It implicitly performs an optimal tradeoff between performance in fitting available data and minimum model complexity. The procedure requires determining Bayesian model evidence (BME), which is the likelihood of the ob...
Article
Full-text available
Choosing between competing models lies at the heart of scientific work, and is a frequent motivation for experimentation. Optimal experimental design (OD) methods maximize the benefit of experiments towards a specified goal. We advance and demonstrate an OD approach to maximize the information gained towards model selection. We make use of so-calle...
Article
Bayesian model averaging (BMA) ranks the plausibility of alternative conceptual models according to Bayes' theorem. A prior belief about each model's adequacy is updated to a posterior model probability based on the skill to reproduce observed data and on the principle of parsimony. The posterior model probabilities are then used as model weights f...
Article
Full-text available
Model averaging makes it possible to use multiple models for one modelling task, like predicting a certain quantity of interest. Several Bayesian approaches exist that all yield a weighted average of predictive distributions. However, often, they are not properly applied which can lead to false conclusions. In this study, we focus on Bayesian Model...
Conference Paper
Statistical multi-model methods have become popular tools for hypothesis testing in ecology and hydrology. Typical goals of such methods are: (1) identifying relevant physical processes, (2) diagnosing model structural deficits, and (3) weighting/combining model predictions to obtain robust ensemble estimates. Bayesian methods in particular offer a...
Conference Paper
Full-text available
Hydrological processes can be represented by various numerical models differing in the considered system components and interactions in the conceptual model and their mathematical and numerical implementation. This leads to uncertainty in model development and model choice, the so-called conceptual uncertainty. Multi-model approaches are statistica...
Article
Full-text available
The choice of data periods for calibrating and evaluating conceptual hydrological models often seems ad-hoc, with no objective guidance on choosing calibration periods that produce the most reliable predictions. We therefore propose to systematically investigate the effects of calibration and validation data choices on parameter identification and...
Article
Model selection and model averaging have become popular tools to address conceptual uncertainty in hydro(geo)logical modeling. Within the last two decades, many different flavors of approaches and implementations have emerged which complicate an easy access to and a thorough understanding of the underlying principles. With the many approaches and a...
Article
Full-text available
When constructing discrete (binned) distributions from samples of a data set, applications exist where it is desirable to assure that all bins of the sample distribution have nonzero probability. For example, if the sample distribution is part of a predictive model for which we require returning a response for the entire codomain, or if we use Kull...
Article
A variety of empirical formulas to predict river bed evolution with hydro-morphodynamic river models exists. Modelers lack objective guidance of how to select the most appropriate one for a specific application. Such guidance can be provided by Bayesian model selection (BMS). Its applicability is however limited by high computational costs. To tran...
Conference Paper
Full-text available
Statistical model selection and averaging techniques have become popular tools in hydrological modelling. These techniques promise objective guidance in (1) identifying relevant physical processes, (2) diagnosing model structural deficits, (3) weighting model predictions to obtain robust ensemble estimates, and (4) performing sensitivity analyses w...
Presentation
Full-text available
Statistical model selection and averaging techniques have become popular tools in hydrological modelling. These techniques promise objective guidance in (1) identifying relevant physical processes, (2) diagnosing model structural deficits, (3) weighting model predictions to obtain robust ensemble estimates, and (4) performing sensitivity analyses w...
Conference Paper
Recent advances in high-resolution techniques for subsurface characterization have the potential to greatly improve our understanding of subsurface flow and transport processes. However, the appropriate interpretation of field data of varying resolution and scale still poses a major challenge to hydrogeologists and modellers. Both conceptual issues...
Conference Paper
Full-text available
When building environmental systems models, we are typically confronted with the questions of how to choose an appropriate model (i.e. which processes to include or neglect) and how to measure its quality. Various metrics have been proposed that shall guide the modeller towards a most robust and realistic representation of the system under study. C...
Conference Paper
Model-based decision support requires justifiable models with good predictive capabilities. This, in turn, calls for a fine adjustment between predictive accuracy (small systematic model bias that can be achieved with rather complex models), and predictive precision (small predictive uncertainties that can be achieved with simpler models with fewer...
Book
Full-text available
Die hydraulische Charakterisierung von Grundwasserleitern ist eine der wichtigsten Untersuchungsschritten bei der Bearbeitung verschiedener umwelt- und ingenieurgeologischer Fragestellungen. Hierfür stehen eine Vielzahl an hydraulischen Feldmethoden zur Verfügung, deren Wahl sich maßgeblich nach den Anforderungen der Fragestellung richtet. In der P...
Conference Paper
Bayesian model selection (BMS) allows to rank competing models according to their posterior probability in the light of observed data. If there is no clear favorite model, the predictive capabilities of the individual models can be combined into a robust weighted average (Bayesian model averaging, BMA), with the posterior model probabilities acting...
Article
Objective measures to compare the adequacy of models can be very useful to guide the development of thermodynamic models. Thermodynamicists are frequently faced with the so-called bias-variance dilemma, where one model may be less accurate in correlating experimental data but more robust in extrapolations than another model. In this work, we use Ba...
Conference Paper
Full-text available
When judging the plausibility and utility of a subsurface flow or transport model, the question of justifiability arises: which level of model complexity can still be justified by the available calibration data? Although it is common sense that more data are needed to reasonably constrain the parameter space of a more complex model, there is a lack...
Thesis
Full-text available
This dissertation aims at improving uncertainty assessment for hydrosystem models subject to uncertainty in model structure, parameters, and forcing terms. In order to explicitly account for conceptual uncertainty (the uncertainty in model choice), Bayesian model averaging (BMA) is used as an integrated modeling framework. BMA is a formal statistic...
Conference Paper
In this study we evaluate the lessons learned about modelling soil/plant systems from analyzing evapotranspiration data, soil moisture and leaf area index. The data were analyzed with advanced tools from the area of Bayesian Model Averaging, model ranking and Bayesian Model Selection. We have generated a large variety of model conceptualizations by...
Conference Paper
Bayesian model averaging (BMA) is a rigorous statistical framework to rank a set of plausible, competing models according to their plausibility, i.e., according to their fit to available data and their complexity. With the resulting posterior model weights, model predictions are averaged to obtain a robust estimate along with uncertainty intervals....
Article
A Bayesian Model Averaging (BMA) framework is presented to evaluate the worth of different observation types and experimental design options for 1) more confidence in model selection and 2) for increased predictive reliability. These two modeling tasks are handled separately, because model selection aims at identifying the most appropriate model wi...
Conference Paper
Full-text available
When confronted with the challenge of selecting one out of several competing conceptual models for a specific modeling task, Bayesian model averaging is a rigorous choice. It ranks the plausibility of models based on Bayes’ theorem, which yields an optimal trade-off between performance and complexity. With the resulting posterior model probabilitie...
Conference Paper
Full-text available
Bayesian model averaging (BMA) ranks and averages a set of plausible, competing models, based on their fit to available data and based on their model complexity. BMA requires determining Bayesian model evidence (BME), which is the likelihood of the observed data integrated over each model parameter space. The BME integral is highly challenging, bec...
Conference Paper
Bayesian model averaging ranks the predictive capabilities of alternative conceptual models based on Bayes’ theorem. The individual models are weighted with their posterior probability to be the best one in the considered set of models. Finally, their predictions are combined into a robust weighted average and the predictive uncertainty can be quan...
Conference Paper
Bayesian model averaging (BMA) combines the predictive capabilities of alternative conceptual models into a robust best estimate and allows the quantification of conceptual uncertainty. The individual models are weighted with their posterior probability according to Bayes' theorem. Despite this rigorous procedure, we see four obstacles to robust mo...
Conference Paper
The objective selection of appropriate models for realistic simulations of coupled soil-plant processes is a challenging task since the processes are complex, not fully understood at larger scales, and highly non-linear. Also, comprehensive data sets are scarce, and measurements are uncertain. In the past decades, a variety of different models have...
Article
Im Stadtgebiet von Stuttgart liegen großräumige Kontaminationen des Grundwassers mit leichtflüchtigen chlorierten Kohlenwasserstoffen (LCKW) vor. Für 25 Einzelstandorte, von denen eine maßgebliche Verunreinigung des Grundwassers ausgeht und die daher als Schlüsselstandorte zur Beschreibung des gesamten Schadensbilds anzusehen sind, werden die Erken...
Article
Full-text available
Heterogeneity of hydrogeological parameters introduces uncertainty into predictions of groundwater flow and contaminant transport processes. Prediction uncertainty can be reduced by conditioning spatially distributed parameter fields to field measurements. In this work, we use the Ensemble Kalman Filter (EnKF) in order to condition random log-condu...
Article
Full-text available
Ensemble Kalman filters (EnKFs) are a successful tool for estimating state variables in atmospheric and oceanic sciences. Recent research has prepared the EnKF for parameter estimation in groundwater applications. EnKFs are optimal in the sense of Bayesian updating only if all involved variables are multivariate Gaussian. Subsurface flow and transp...
Thesis
Full-text available
Uncertain hydrogeological parameters compromise the reliability of predictions for contaminant spreading in the subsurface. In this work, an inverse stochastic modeling framework is used for parameter estimation. This allows to include available measurement data and then quantify the uncertainty of model prognoses and determine exceedance probabili...

Questions and Answers

Question & Answers (2)

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Projects

Projects (9)
Project
Rodrigo Rojas from CSIRO and Anneli Guthke from University of Stuttgart are guest-editing a special issue on ‘Model uncertainty in water science: conceptualization, assessment and communication’ in the Journal Water (http://www.mdpi.com/journal/water/special_issues/water_model_uncertainty). We are welcoming contributions covering methodological and applied aspects with a special emphasis on uncertainty quantification and moving beyond into risk assessment, decision-making and water management. Deadline for submission is end of May, the journal is open access. More details can be found in the uploaded PDF. Looking forward to your contributions, please also spread the word among interested colleagues and friends!
Project
This course material provides an introduction to groundwater flow and solute transport modeling with applications by Randolf Rausch and Anneli Guthke, 2018.
Project
Accounting for various sources of uncertainty when evaluating and comparing the adequacy of competing models.