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Quantifying Uncertainty (quantifying + uncertainty)
Selected AbstractsBridging the gap between field data and global models: current strategies in aeolian researchEARTH SURFACE PROCESSES AND LANDFORMS, Issue 4 2010Joanna Bullard Abstract Modern global models of earth-atmosphere-ocean processes are becoming increasingly sophisticated but still require validation against empirical data and observations. This commentary reports on international initiatives amongst aeolian researchers that seek to combine field-based data sets and geomorphological frameworks for improving the quality of data available to constrain and validate global models. These include a second iteration of the Dust Indicators and Records from Terrestrial Marine Palaeoenvironments (DIRTMAP2) database, the Digital Atlas of Sand Seas and Dunefields of the World and a new geomorphology-based land surface map produced by the QUEST (Quantifying Uncertainties in the Earth System) Working Group on Dust. Copyright © 2010 John Wiley & Sons, Ltd. [source] Borehole-guided AVO analysis of P-P and P-S reflections: Quantifying uncertainty on density estimatesGEOPHYSICAL PROSPECTING, Issue 5 2006Hugues A. Djikpesse ABSTRACT Seismic properties of isotropic elastic formations are characterized by the three parameters: acoustic impedance, Poisson's ratio and density. Whilst the first two are usually well estimated by analysing the amplitude variation with angle (AVA) of reflected P-P waves, density is known to be poorly resolved. However, density estimates would be useful in many situations encountered in oil and gas exploration, in particular, for minimizing risks in looking ahead while drilling. We design a borehole seismic experiment to investigate the reliability of AVA extracted density. Receivers are located downhole near the targeted reflectors and record reflected P-P and converted P-S waves. A non-linear, wide-angle-based Bayesian inversion is then used to access the a posteriori probability distributions associated with the estimation of the three isotropic elastic parameters. The analysis of these distributions suggests that the angular variation of reflected P-S amplitudes provides additional substantial information for estimating density, thus reducing the estimate uncertainty variance by more than one order of magnitude, compared to using only reflected P-waves. [source] Quantifying uncertainty in estimates of C emissions from above-ground biomass due to historic land-use change to cropping in AustraliaGLOBAL CHANGE BIOLOGY, Issue 8 2001Damian J. Barrett Abstract Quantifying continental scale carbon emissions from the oxidation of above-ground plant biomass following land-use change (LUC) is made difficult by the lack of information on how much biomass was present prior to vegetation clearing and on the timing and location of historical LUC. The considerable spatial variability of vegetation and the uncertainty of this variability leads to difficulties in predicting biomass C density (tC ha,1) prior to LUC. The issue of quantifying uncertainties in the estimation of land based sources and sinks of CO2, and the feasibility of reducing these uncertainties by further sampling, is critical information required by governments world-wide for public policy development on climate change issues. A quantitative statistical approach is required to calculate confidence intervals (the level of certainty) of estimated cleared above-ground biomass. In this study, a set of high-quality observations of steady state above-ground biomass from relatively undisturbed ecological sites across the Australian continent was combined with vegetation, topographic, climatic and edaphic data sets within a Geographical Information System. A statistical model was developed from the data set of observations to predict potential biomass and the standard error of potential biomass for all 0.05° (approximately 5 × 5 km) land grid cells of the continent. In addition, the spatial autocorrelation of observations and residuals from the statistical model was examined. Finally, total C emissions due to historic LUC to cultivation and cropping were estimated by combining the statistical model with a data set of fractional cropland area per land grid cell, fAc (Ramankutty & Foley 1998). Total C emissions from loss of above-ground biomass due to cropping since European colonization of Australia was estimated to be 757 MtC. These estimates are an upper limit because the predicted steady state biomass may be less than the above-ground biomass immediately prior to LUC because of disturbance. The estimated standard error of total C emissions was calculated from the standard error of predicted biomass, the standard error of fAc and the spatial autocorrelation of biomass. However, quantitative estimates of the standard error of fAc were unavailable. Thus, two scenarios were developed to examine the effect of error in fAc on the error in total C emissions. In the first scenario, in which fAc was regarded as accurate (i.e. a coefficient of variation, CV, of fAc = 0.0), the 95% confidence interval of the continental C emissions was 379,1135 MtC. In the second scenario, a 50% error in estimated cropland area was assumed (a CV of fAc = 0.50) and the estimated confidence interval increased to between 350 and 1294 MtC. The CV of C emissions for these two scenarios was 25% and 29%. Thus, while accurate maps of land-use change contribute to decreasing uncertainty in C emissions from LUC, the major source of this uncertainty arises from the prediction accuracy of biomass C density. It is argued that, even with large sample numbers, the high cost of sampling biomass carbon may limit the uncertainty of above-ground biomass to about a CV of 25%. [source] Quantifying uncertainty using robustness analysis in the application of ORESTE to sewer rehabilitation projects prioritization,Brussels case studyJOURNAL OF MULTI CRITERIA DECISION ANALYSIS, Issue 3-4 2009Eliseo Ana Jr Abstract Sewer systems are considered extremely important components of the urban water infrastructure due to their function and capital-intensive nature. These systems, however, have been undergoing aging and deterioration, thus needing repair or rehabilitation. Historically, the budgets for sewer rehabilitation are often scarce and too limited to address the requirements, requiring utility managers to prioritize the competing projects. In this paper, the application of ORESTE to the prioritization of sewer rehabilitation projects for the Brussels, Belgium network was demonstrated. The 43 proposed projects were ranked based on a set of 16 criteria. In addition, a methodology was introduced to investigate the robustness of the ORESTE solution. The inclusion of the robustness analysis into the technique allowed for the quantification of the uncertainties associated with the priority rankings. This type of information is very important in developing confidence among decision makers as to their decision on the priority ranking of sewer rehabilitation projects. Copyright © 2010 John Wiley & Sons, Ltd. [source] Quantifying uncertainty in the biospheric carbon flux for England and WalesJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 1 2008Marc Kennedy Summary., A crucial issue in the current global warming debate is the effect of vegetation and soils on carbon dioxide (CO2) concentrations in the atmosphere. Vegetation can extract CO2 through photosynthesis, but respiration, decay of soil organic matter and disturbance effects such as fire return it to the atmosphere. The balance of these processes is the net carbon flux. To estimate the biospheric carbon flux for England and Wales, we address the statistical problem of inference for the sum of multiple outputs from a complex deterministic computer code whose input parameters are uncertain. The code is a process model which simulates the carbon dynamics of vegetation and soils, including the amount of carbon that is stored as a result of photosynthesis and the amount that is returned to the atmosphere through respiration. The aggregation of outputs corresponding to multiple sites and types of vegetation in a region gives an estimate of the total carbon flux for that region over a period of time. Expert prior opinions are elicited for marginal uncertainty about the relevant input parameters and for correlations of inputs between sites. A Gaussian process model is used to build emulators of the multiple code outputs and Bayesian uncertainty analysis is then used to propagate uncertainty in the input parameters through to uncertainty on the aggregated output. Numerical results are presented for England and Wales in the year 2000. It is estimated that vegetation and soils in England and Wales constituted a net sink of 7.55 Mt C (1 Mt C = 1012 g of carbon) in 2000, with standard deviation 0.56 Mt C resulting from the sources of uncertainty that are considered. [source] | ||||||||||