A Comparison of Bayesian Methods for Uncertainty Analysis in Hydraulic and Hydrodynamic Modeling

We evaluate and compare the performance of Bayesian Monte Carlo (BMC), Markov chain Monte Carlo (MCMC), and the Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis in hydraulic and hydrodynamic modeling (HHM) studies. The methods are evaluated in a synthetic 1D wave routing exercise based on the diffusion wave model, and in a multidimensional hydrodynamic study based on the Environmental Fluid Dynamics Code to simulate estuarine circulation processes in Weeks Bay, Alabama. Results show that BMC and MCMC provide similar estimates of uncertainty. The posterior parameter densities computed by both methods are highly consistent, as well as the calibrated parameter estimates and uncertainty bounds. Although some studies suggest that MCMC is more efficient than BMC, our results did not show a clear difference between the performance of the two methods. This seems to be due to the low number of model parameters typically involved in HHM studies, and the use of the same likelihood function. In fact, for these studies, the implementation of BMC results simpler and provides similar results to MCMC. The results of GLUE are, on the other hand, less consistent to the results of BMC and MCMC in both applications. The posterior probability densities tend to be flat and similar to the uniform priors, which can result in calibrated parameter estimates centered in the parametric space.     

Application of an entropy-based Bayesian optimization technique to the redesign of an existing monitoring network for single air pollutants

We apply the entropy-based Bayesian optimizing approach of Le and Zidek to the spatial redesign of the extensive air pollution monitoring network operated by Metro Vancouver, in the Lower Fraser Valley, British Columbia. This method is chosen because of its statistical sophistication, relative to other possible approaches, and because of the very rich, two-decade long data record available from this network. The redesign analysis is applied to ozone, carbon monoxide and PM_2.5 pollutants.     

Value of information analysis for adequate monitoring of carbon dioxide storage in geological reservoirs under uncertainty

As monitoring is essential for the proper management of geological storage of carbon dioxide (CO₂), the ability to value information from monitoring is indispensable to adequately design a monitoring program. It is necessary to judge whether the expected improvement in management is worth the cost of monitoring. The value of information (VOI) is closely related to the possible increase in expected utility gained by gathering the information, the concept of which can be applied to such judgement. Although VOI analysis has been extensively studied in the context of decision analysis, its application to the management of carbon dioxide capture and storage (CCS) operations is rare. This paper introduces and discusses the methodology of VOI analyses in the context of monitoring CO₂ storage. A motivating problem with discrete probabilities is used to illustrate the concept of VOI. It is demonstrated that information is not always of value; for information to be worthwhile, monitoring under uncertainty must satisfy certain conditions. This concept is then extended to continuous probability distributions. The effects of prior uncertainty and information reliability on the VOI are examined. It is shown that an excessive improvement in information accuracy yields little value and that the optimal level of reliability can be inferred. VOI analyses provide quantitative insights into the value of information-gathering activities and therefore can be an objective means to adequately design and impartially justify a monitoring program.     

Impacts of a participatory approach to developing national level sustainable development indicators in Finland

The paper explores the role of a participatory approach in the outcome of the Finnish sustainable development indicator (SDI) exercise in 1998-2002. The process is analysed through three main objectives: to achieve stronger democracy, better quality of the end product and a more effective process. The analysis is further structured by a set of criteria needed for successful participation and differentiation of types of participants. The criteria comprise three main aspects: fairness, competence and social learning. In addition to the normally mentioned stakeholders (e.g. citizens and interest groups) participants also include experts and civil servants. Using the set of criteria above the participatory approach of the Finnish SDI process is then evaluated, and in the light of this evaluation the paper also discusses the specifications needed as evaluation criteria for national level policy programme processes like developing the SDIs. The results are based on documentation of the indicator task force meetings, written comments and a study of the putative end-users conducted after the publication of the indicators. The results show that the intense and broad participation of experts and civil servants increased the competence of the outcome and led to greater efficiency in working methods. However, this led to technocratic participation, absence of democratic participation and absence of social learning. Thus the ultimate goal of SDIs to contribute to achieving sustainability was not reached.