Analysing uncertainty

Risk Analysis, 1999

Water science and technology : a journal of the International Association on Water Pollution Research, 2005

Understanding the limitations of environmental data is essential both for managing environmental systems effectively and for encouraging the responsible use of scientific research when knowledge is limited and priorities are varied. Using a combination of quantitative and qualitative techniques for assessing probabilities, and acknowledging the importance of scenarios where probabilities cannot be determined, an integrated methodology is presented for handling uncertainties about environmental data. The methodology is based on a fourfold distinction between the empirical quality of data (and the ancillary information, such as 'scale', required to interpret this), the sources of uncertainty in data, the 'fitness for use' of the data, and the quality or 'goodness' of an uncertainty model.

Eos, Transactions American …, 2007

Although environmental processes at large scales are to a great degree the resultant of processes at smaller scales, models representing these processes can vary considerably from scale to scale. There are three main reasons for this. Firstly, different processes dominate at different scales, and so different processes are ignored in the simplification step of the model development. Secondly, input data are often absent or of a much lower quality at larger scales, which results in a tendency to use simpler, empirical models at the larger scale. Third, the support of the inputs and outputs of a model changes with change of scale, and this affects the relationships between them. Given these reasons for using different models at different scales, application of a model developed at a specific scale to a larger scale should be treated with care. Instead, models should be modified to suit the larger scale, and for this purpose uncertainty analyses can be extremely helpful. If upscaling disturbed the balance between the contributions of input and model error to the output error, then an uncertainty analysis will show this. Uncertainty analysis will also show how to restore the balance. In practice, application of uncertainty analysis is severely hampered by difficulties in the assessment of input and model error. Knowledge of the short distance spatial variability is of paramount importance to input error assessment with a change of support, but current geographical databases rarely convey this type of information. Model error can only be estimated reliably by validation, but this is not easy because the support of model predictions and validation measurements is usually not the same.

2011

This document is intended to provide an overview of the wide variety of terms and concepts that are being used when discussing uncertainty. This document has been written in response to an expressed need by the Knowledge for Climate theme 2 consortium to get some clarity on the terminology that is being used when discussing uncertainty in various scientific and policy domains. This document has been written by the researchers working on projects belonging to work package 5 on uncertainty.

International Journal of Risk Assessment and Management, 2005

In this paper theoretical and practical aspects of uncertainty in environmental laboratory analysis are presented and discussed. A more detailed theory of uncertainty is described, with special emphasis on the factors which contribute to the understanding of this in order to obtain accurate environmental laboratory data analysis. A full discussion is reported relating to approaches used to evaluate uncertainty, particularly the so called bottom-up' and`top-down' approaches. Relevant aspects of uncertainty related to instrumental analysis and calibration are discussed. Problems arising from linear and non linear calibration and outlier identification and their relationships with uncertainties were also fully examined.

Analytica Chimica Acta, 2011

Evaluation of analytical results reliability is of core importance as crucial decisions are taken with them. From the various methodologies to evaluate the fitness of purpose of analytical methods, overall measurement uncertainty estimation is more and more applied. Overall measurement uncertainty allows to combine simultaneously the remaining systematic influences to the random sources of uncertainty and allows assessing the reliability of results generated by analytical methods. However there are various interpretations on how to estimate overall measurement uncertainty, and thus various models for estimating it. Each model together with its assumptions has great impacts on the risks to abusively declare that analytical methods are suitable for their intended purpose. This review paper aims at (i) summarizing the various models used to estimate overall measurement uncertainty, (ii) provide their pros and cons, (iii) review the main areas of application and (iv) as a conclusion provide some recommendations when evaluating overall measurement uncertainty.

Hydrological Sciences Journal, 2008

An uncertainty assessment method for evaluating models, the Sources of UNcertainty GLobal Assessment using Split SamplES (SUNGLASSES), is presented, which assesses predictive uncertainty that is not captured by parameter or other input uncertainties. The method uses the split sample approach to generate a quantitative estimate of the fit-for-purpose of the model, thus focusing on the purpose for which the model is used. It operates by comparing the output to be used for decision making to its observed counterpart and the associated uncertainty. The described method is applied on a Soil Water Assessment Tool (SWAT) model of Honey Creek, a tributary of the Sandusky catchment in Ohio, USA. Water flow and sediment loads are analysed. In this case study the uncertainty estimated by the proposed method is much larger than the typically estimated parameter uncertainty.

Springer Tracts in Mechanical Engineering, 2021

This chapter describes the various approaches to analyse, quantify and evaluate uncertainty along the phases of the product life cycle. It is based on the previous chapters that introduce a consistent classification of uncertainty and a holistic approach to master the uncertainty of technical systems in mechanical engineering. Here, the following topics are presented: the identification of uncertainty by modelling technical processes, the detection and handling of data-induced conflicts, the analysis, quantification and evaluation of model uncertainty as well as the representation and visualisation of uncertainty. The different approaches are discussed and demonstrated on exemplary technical systems.

Nonrenewable Resources, 2004

For many decades the mining industry regarded resources/reserves estimation and classification as a mere calculation requiring basic mathematical and geological knowledge. Most methods were based on geometrical procedures and spatial data distribution. Therefore, uncertainty associated with tonnages and grades either were ignored or mishandled, although various mining codes require a measure of confidence in the values reported. Traditional methods fail in reporting the level of confidence in the quantities and grades. Conversely, kriging is known to provide the best estimate and its associated variance. Among kriging methods, Ordinary Kriging (OK) probably is the most widely used one for mineral resource/reserve estimation, mainly because of its robustness and its facility in uncertainty assessment by using the kriging variance. It also is known that OK variance is unable to recognize local data variability, an important issue when heterogeneous mineral deposits with higher and poorer grade zones are being evaluated. Altenatively, stochastic simulation are used to build local or global uncertainty about a geological attribute respecting its statistical moments. This study investigates methods capable of incorporating uncertainty to the estimates of resources and reserves via OK and sequential gaussian and sequential indicator simulation The results showed that for the type of mineralization studied all methods classified the tonnages similarly. The methods are illustrated using an exploration drill hole data sets from a large Brazilian coal deposit.

Geoderma, 2001

This paper addresses the issue of modelling the uncertainty about the value of continuous soil Ž . attributes, at any particular unsampled location local uncertainty as well as jointly over several Ž . locations multiple-point or spatial uncertainty . Two approaches are presented: kriging-based and Ž . simulation-based techniques that can be implemented within a parametric e.g. multi-Gaussian or Ž . non-parametric indicator frameworks. As expected in theory and illustrated by case studies, the two approaches yield similar models of local uncertainty, yet the simulation-based approach has Ž . several advantages over kriging: 1 it provides a model of spatial uncertainty, e.g. the probability Ž . that a given threshold is exceeded jointly at several locations can be readily computed, 2 Ž . conditional cumulative distribution function ccdf for supports larger than the measurement Ž . support e.g. remediation units or flow simulator cells can be numerically approximated by the cumulative distribution of block simulated values that are obtained by averaging values simulated Ž . within the block, and 3 the set of realizations allows one to study the propagation of uncertainty through global GIS operations or complex transfer functions, such as flow simulators that consider many locations simultaneously rather than one at a time.