Repeatability and uncertainty analyses of NASA/MSFC light gas gun test data

This Final Report presents an overview of the impact tests performed at NASA/MSFC in the time period 1985 to 1991 and the results of phenomena repeatability and data uncertainty studies performed using the information obtained from those tests. An analysis of the data from over 400 tests conducted between 1989 and 1991 was performed to generate a database to supplement the Hypervelocity Impact Damage Database developed under a previous effort

Sensitivity and Uncertainty Analyses of an Urban Forest Structure and Function Model

Urban forest models can quantify forest structure and benefits, and are frequently employed in decision-making. This dissertation first reviewed case studies of urban forest modeling practices over the past two-decades, compared the similarities and differences among different models, and summarized the current trends and gaps in the field of urban forest modeling. One gap is the lack of uncertainty assessments for model output. To address this gap, this dissertation performed sensitivity and uncertainty analyses for a popular urban forest model, i-Tree Eco. Based on a case study in New York City, the sensitivity analyses found that the most important input variables are genus for isoprene and monoterpene emissions, DBH for carbon estimators, and leaf area index, temperature, and photosynthetically active radiation for dry deposition estimators. The uncertainty analyses addressed uncertainties associated with the entire i-Tree Eco modeling process, from input data collection, to the characterization of urban tree structure, to the subsequent estimators of the ecosystem services of urban trees. Uncertainty magnitudes were quantified by employing bootstrap and Monte Carlo simulations, and the three sources of uncertainty, input, model, and sampling, were aggregated to derive an estimator of total uncertainty. Through case studies in 16 cities across the United States, the average magnitude of total uncertainty across the 16 cities was 12.4% for leaf area, 12.4% for leaf biomass, 13.5% for carbon storage, 11.1% for carbon sequestration, 40.7% for isoprene emissions, and 25.0% for monoterpene emissions. For leaf and carbon estimators, the total uncertainty is primarily driven by sampling uncertainty, while the magnitudes of sampling, input and model uncertainty are similar across the 16 study cities. In contrast, input, sampling, and model uncertainties all contribute similarly to the total uncertainty for isoprene and monoterpene emission estimators, and there are larger variations in these three sources of uncertainty across the 16 study cities. To reduce overall uncertainty, future studies should develop more accurate urban-, local-, and species-specific allometric relationships, improve the spatial representation of meteorological variables, develop more extensive and accurate local-scale measurements to calibrate and verify models, and improve sampling strategies

Time-dependent sensitivity and uncertainty analyses of an agro-climatic model for the water status management of vineyard

International audienceThis work describes the global sensitivity analysis (SA) of an agro-climatic model embedded in a decision support system (DSS) for the water status management of vineyard in the Languedoc-Roussillon region, France

Age-Related Differences in Susceptibility to Carcinogenesis. II. Approaches for Application and Uncertainty Analyses for Individual Genetically Acting Carcinogens

In an earlier report we developed a quantitative likelihood-based analysis of the differences in sensitivity of rodents to mutagenic carcinogens across three life stages (fetal, birth to weaning, and weaning to 60 days) relative to exposures in adult life. Here we draw implications for assessing human risks for full lifetime exposures, taking into account three types of uncertainties in making projections from the rodent data: uncertainty in the central estimates of the life-stage–specific sensitivity factors estimated earlier, uncertainty from chemical-to-chemical differences in life-stage–specific sensitivities for carcinogenesis, and uncertainty in the mapping of rodent life stages to human ages/exposure periods. Among the uncertainties analyzed, the mapping of rodent life stages to human ages/exposure periods is most important quantitatively (a range of several-fold in estimates of the duration of the human equivalent of the highest sensitivity “birth to weaning” period in rodents). The combined effects of these uncertainties are estimated with Monte Carlo analyses. Overall, the estimated population arithmetic mean risk from lifetime exposures at a constant milligrams per kilogram body weight level to a generic mutagenic carcinogen is about 2.8-fold larger than expected from adult-only exposure with 5–95% confidence limits of 1.5-to 6-fold. The mean estimates for the 0- to 2-year and 2- to 15-year periods are about 35–55% larger than the 10- and 3-fold sensitivity factor adjustments recently proposed by the U.S. Environmental Protection Agency. The present results are based on data for only nine chemicals, including five mutagens. Risk inferences will be altered as data become available for other chemicals

Great SCO2T! Rapid tool for carbon sequestration science, engineering, and economics

CO2 capture and storage (CCS) technology is likely to be widely deployed in coming decades in response to major climate and economics drivers: CCS is part of every clean energy pathway that limits global warming to 2C or less and receives significant CO2 tax credits in the United States. These drivers are likely to stimulate capture, transport, and storage of hundreds of millions or billions of tonnes of CO2 annually. A key part of the CCS puzzle will be identifying and characterizing suitable storage sites for vast amounts of CO2. We introduce a new software tool called SCO2T (Sequestration of CO2 Tool, pronounced "Scott") to rapidly characterizing saline storage reservoirs. The tool is designed to rapidly screen hundreds of thousands of reservoirs, perform sensitivity and uncertainty analyses, and link sequestration engineering (injection rates, reservoir capacities, plume dimensions) to sequestration economics (costs constructed from around 70 separate economic inputs). We describe the novel science developments supporting SCO2T including a new approach to estimating CO2 injection rates and CO2 plume dimensions as well as key advances linking sequestration engineering with economics. Next, we perform a sensitivity and uncertainty analysis of geology combinations (including formation depth, thickness, permeability, porosity, and temperature) to understand the impact on carbon sequestration. Through the sensitivity analysis we show that increasing depth and permeability both can lead to increased CO2 injection rates, increased storage potential, and reduced costs, while increasing porosity reduces costs without impacting the injection rate (CO2 is injected at a constant pressure in all cases) by increasing the reservoir capacity.Comment: CO2 capture and storage; carbon sequestration; reduced-order modeling; climate change; economic

Advances and visions in large-scale hydrological modelling: findings from the 11th Workshop on Large-Scale Hydrological Modelling

Large-scale hydrological modelling has become increasingly wide-spread during the last decade. An annual workshop series on large-scale hydrological modelling has provided, since 1997, a forum to the German-speaking community for discussing recent developments and achievements in this research area. In this paper we present the findings from the 2007 workshop which focused on advances and visions in large-scale hydrological modelling. We identify the state of the art, difficulties and research perspectives with respect to the themes "sensitivity of model results", "integrated modelling" and "coupling of processes in hydrosphere, atmosphere and biosphere". Some achievements in large-scale hydrological modelling during the last ten years are presented together with a selection of remaining challenges for the future

Sensitivity and Uncertainty analyses on a DELPHIN model: The impact of material properties on moisture in a solid brick wall

This paper presents sensitivity and uncertainty analyses on a DELPHIN model, which is representative of a case study wall in real climatic conditions. Results of the Differential Sensitivity Analysis (DSA) show properties governing liquid water transported into, through and stored in the wall impact most on moisture accumulation, affecting relative humidity (RH) outputs by 10 – 35% at three different locations in the wall. Parameters affecting vapour transport into the room also influence RH outputs at the inner location, but less than rain amount and rain exchange coefficient. A probabilistic uncertainty study is then used to explore key material functions, parameterised as four sets of co-ordinates and varied randomly. The correlation between the parameter inputs and the resulting change in RH is assessed. There are some surprising divergences from the DSA, including the significance of moisture storage in the plaster layer in the presence of liquid. Low correlation coefficients suggest numbers of variables could be reduced to further clarify the effects of these parameters, and interesting questions are raised on the parameterisation of material functions to represent the uncertainty in the characterisation of real walls

Statistical emulation of a tsunami model for sensitivity analysis and uncertainty quantification

Due to the catastrophic consequences of tsunamis, early warnings need to be issued quickly in order to mitigate the hazard. Additionally, there is a need to represent the uncertainty in the predictions of tsunami characteristics corresponding to the uncertain trigger features (e.g. either position, shape and speed of a landslide, or sea floor deformation associated with an earthquake). Unfortunately, computer models are expensive to run. This leads to significant delays in predictions and makes the uncertainty quantification impractical. Statistical emulators run almost instantaneously and may represent well the outputs of the computer model. In this paper, we use the Outer Product Emulator to build a fast statistical surrogate of a landslide-generated tsunami computer model. This Bayesian framework enables us to build the emulator by combining prior knowledge of the computer model properties with a few carefully chosen model evaluations. The good performance of the emulator is validated using the Leave-One-Out method

Application of Sensitivity and Uncertainty Analyses to Linear Time-Invariant Compartmental Models

Chapter 1 reviews the fundamental aspects of modelling and introduces sensitivity and uncertainty issues. Chapter 2 first introduces and reviews the theory of linear, time-invariant compartmental models, then describes a number of methods used to solve model state equations analytically and numerically in order to make predictions. This chapter also describes the methodology of numerous sensitivity analysis methods. In Chapter 3, application of various sensitivity analysis techniques to two 8- compartment global carbon cycle models is presented. For ease of comparison, a measure of similarity between the sensitivity conclusions from different methods is defined based on the top 10 ranked input factors according to each method for each output variable (i.e. for each compartment at chosen time points). Chapter 4 presents the results of the application of various sensitivity analysis methods including non-parametric methods to a more complex 25-compartment global carbon cycle model. An overall informal comparison indicates that the 8-compartment global carbon cycle models used in Chapters 3 and 4 are optimal with respect to efficiency (i.e. both are simple and model codes are not very time-consuming to run), but in return do not have a high degree of stability and reliability since they do not adopt biological and chemical processes. As for the 25-compartment model, it is more complex and more costly to run. These chapters review the applicability of the sensitivity analysis methods to these models which has steady-state constrain. Chapter 5 explores various sources of uncertainty and presents results of uncertainty analysis applied to the three global carbon cycle models that are used in Chapters 3 and 4. Here, we partition the overall prediction uncertainty of an output variable into different components of uncertainty. Finally, Chapter 6 presents conclusions and main findings of the thesis