Delineating spatial patterns of precipitation isotopes (“isoscapes”) is important for studies including the hydrology of terrestrial systems, present and past interpretations of climate, and tracer-aided hydrological modelling, among others. However, the extent to which precipitation isoscapes can be predicted across Canada has not been fully articulated. This thesis combines isotopes in precipitation (δ18Oppt) observations from two regional and one global network to create long term and time series precipitation isoscapes for Canada and the northern United States. Multi-linear regressions of a small suite of geographic and climate variables generate the best performing long-term and seasonal models of δ18Oppt. These models are used to develop long term isoscapes for Canada, which capture the general spatial and seasonal trends in δ18Oppt, showing an improvement upon results from previous studies using global models. Building upon long-term δ18Oppt prediction, δ18Oppt observations alongside climatological and geographic predictors are used to create empirical time series prediction models. Five regionalization approaches are used to separate the study domain into isotope zones to explore the effect of spatial grouping on simulations. Generally, the models capture the timing and magnitude of intra-annual (seasonal) δ18Oppt cycles across the study domain while simulating moderate inter-annual variation; however often fail to capture the anomalies in observed δ18Oppt. Uncertainty in predictions is quantified spatially and temporally, and the Köppen-Geiger (Kpn) regionalization is selected as the preferred regionalization scheme for future applications due to adequate model performance and lack of border issues at regional boundaries. Finally, estimates of monthly δ18Oppt from Kpn models, long term annual averages, and daily REMOiso output are used to force an isotope-enabled hydrological model, isoWATFLOOD, in the Fort Simpson Basin, NWT, Canada. Results show streamflow simulations are not significantly impacted by choice of δ18Oppt input; however, oxygen-18 in streamflow and the internal apportionment of water (and model parameterizations) are impacted, particularly during large precipitation and snowmelt events. This work shows how isoWATFLOOD can be used in regions with limited δ18Oppt observations, and that the model can be of value in such regions. This study reinforces that a tracer-aided modelling approach works towards diagnosing issues surrounding model equifinality. / February 2017

Sustainable approaches have been extensively proposed in product, process and system levels. However, a lack of applicable solutions for these methods is identified in the existing research. This research considers uncertainties affecting sustainable systems and comprehensively discusses the need for the optimal design in product and system levels under uncertainty. Based on the economic, social and environmental requirements of a sustainable product, and uncertainties in engineering systems, two innovative methods are proposed. The methods, including agent-based modeling (ABM) and Big Data, quantify effects of users’ preference changes as a significant uncertainty source in a product design process. The effect of quantified uncertainties on the product sustainability is then evaluated, and solutions to reduce the effects are developed. Through a novel control engineering method, uncertainties are modeled in the design process of a product. Using two mathematical models, the cost and environmental impacts in the design process are minimized under users’ preference changes. The models search for an optimal number of iterations in the design process to achieve a sustainable solution. The methods have been extended to model and optimize the sustainable system design under uncertainties. Design of Eco-Industrial Parks (EIPs) is a practical and scientific solution to achieve sustainable industries. To improve the feasibility of flow exchanges between industries in an EIP under several uncertainties, this research provides a perspective analysis for establishing flow exchanges between industries. The sources of uncertainties in the EIPs are then comprehensively studied, and research gaps are highlighted. Finally, models to optimize flow exchanges between industries are presented and the validity of models is evaluated using real data. A major is including all sustainability pillars in the proposed approach. The research addresses users’ preferences to highlight the role of individuals in the society. Moreover, the economic and environmental objective functions have been considered for optimal decision making in the design process. This research underlines the role of uncertainty studies in the sustainable system design. Multiple classifications, perspective analysis, and optimization objectives are presented to help decision makers with the optimal design of sustainable systems under uncertainties. / February 2017

Drawing from social network theory, scholars have identified two ways in which social ties influence venture capital investment decisions: directly through personal ties and indirectly through status hierarchies. Previous research has examined these effects independently. Our study is the first to perform a joint examination of the role of social ties and status hierarchies in venture capital decision making. We examine the relative importance of these two mechanisms through an adaptive choice-based conjoint experiment comprising of 3,132 investment decisions made by 86 venture capitalists from the United States and Europe. Our experimental context allows us to explore whether, under high levels of market uncertainty, strong personal ties exert more influence over investment decisions than the presence of a high-status investor in the deal. We also explore the moderating effects that market structure and experience play in shaping these decision processes. Our findings reveal that personal ties are more important in venture capital decision making when compared to the relative status of other venture capital firms participating in the investment syndicate. Building on our main findings, we show that the influence of personal ties is less pronounced in the European investment community, as compared to more densely networked U.S. investors. We also find a U-shaped relationship between venture capitalist experience and the influence of personal networks on investment decisions. (authors' abstract)

The recent transformation of European electricity markets with increasing generation from intermittent renewables brings about many challenges. Among them, decaying wholesale prices, partly due to support schemes for renewables, may send insufficient investment signals for other technologies. We investigate the investment decision in a structural equation based on the Tobin's q-model, which we extend by both industry- and firm-technology-specific uncertainty. We utilize rich and novel data at the disaggregated firm generation technology level of European electricity generating firms for the period 2006-2014. Our results show that investment in any generation technology follows market incentives despite sunk and irreversible capital, confirming the implications of the q-model. Moreover, while firm-technology-specific uncertainty decreases firms' investment activity, especially in coal and gas, aggregate uncertainty triggers firms' investment. Our results raise concerns about system reliability in the long run since conventional technologies still serve as a flexible system back-up. (authors' abstract) / Series: Department of Economics Working Paper Series

eXfiltration Advanced Persistent Threats (XAPTs) increasingly account for incidents concerned with critical information exfiltration from High Valued Targets (HVT's) by terrorists, cyber criminals or enemy states. Existing Cyber Defence frameworks and data fusion models do not adequately address (i) the multi-stage nature of XAPTs and (ii) the uncertainty and conflicting information associated with XAPTs. A new data fusion theory, called the Markov Multi-phase Transferable Belief Model (MM-TBM) is developed, for tracking and predicting XAPTs. MM-TBM expands the attack kill-chain model to attack trees and introduces a novel approach for combining various sources of cyber evidence, which takes into account the multi-phased nature of XAPTs and the characteristics of the cyberspace. As a data fusion theory, MM-TBM constitutes a novel approach for performing hypothesis assessment and evidence combination across phases, by means of a new combination rule, called the Multi-phase Combination Rule with conflict Reset (MCR2). This is the first combination rule in the field of data fusion that formalises a new method for combining evidence from multiple, causally connected hypotheses spaces and eliminating the bias from preceding phases of the kill-chain. Moreover, this is the first time a data fusion theory utilises the conflict mass m(Ø) for identifying paradoxes. In addition, a diagnostic formula for managing missing pieces of evidence within attack trees is presented. MM-TBM is designed, developed and evaluated using a Design Science Research approach within two iterations. Evaluation is conducted in a relevant computer network environment using scenario-based testing. The experimental design has been reviewed and approved by Cyber Security Subject Matter Experts from MoD’s Defence Science Technology Laboratory and Airbus Group. The experimental results validate the novel capabilities introduced by the new MM-TBM theory to Cyber Defence in the presence of information clutter, conflict and congestion. Furthermore, the results underpin the importance of selecting an optimal sampling policy to effectively track and predict XAPTs. This PhD bridges the gaps in the body of knowledge concerned with multi-phase fusion under uncertainty and Cyber SA against XAPTs. MM-TBM is a novel mathematical fusion theory for managing applications that existing fusion models do not address. This research has demonstrated MM-TBM enables the successful Tracking and Prediction of XAPTs to deliver an enhanced Cyber SA capability.

Approved for public release; distribution in unlimited. / As mesoscale models increase in resolution there is a greater need to understand predictability on smaller scales. The predictability of a model is related to forecast skill. It is possible that the uncertainty of one scale of motion can affect the other scales due to the nonlinearity of the atmosphere. Some suggest that topography is one factor that can lead to an increase of forecast skill and therefore predictability. This study examines the uncertainty of a mesoscale model and attempts to characterize the predictability of the wind field. The data collected is from the summer, when the synoptic forcing is relatively benign. Mesoscale Model 5 (MM5) lagged forecasts are used to create a three-member ensemble over a 12-hour forecast cycle. The differences in these forecasts are used to determine the spread of the wind field. Results show that some mesoscale features have high uncertainty and others have low uncertainty, shedding light on the potential predictability of these features with a mesoscale model. Results indicate that topography is a large source of uncertainty. This is seen in all data sets, contrary to other studies. The ability of the model to properly forecast the diurnal cycle also impacted substantially on the character and evolution of forecast spread. The persistent mesoscale features were represented reasonably well, however the detailed structure of these features had a fair amount of uncertainty. / Lieutenant Junior Grade, United States Navy

Accurate situational assessment is key to any decision maker and especially crucial in military command and control, air traffic control, and complex system decision making. Endsley described three dependent levels of situational awareness, (1) perception, (2) understanding, and (3) projection. This research was focused on Endsley's second-level situational awareness (understanding) as it applies to service-oriented information technology environments in the context of the Semantic Web. Specifically, this research addressed the problem of developing accurate situational assessments related to the status or health of information technology (IT) services, especially composite, dynamic IT services, when some of Endsley's first level (perceived) information was inaccurate or incomplete. Research had not adequately addressed the problem of how to work with inaccuracy and situational awareness information in order to produce accurate situational assessments for Semantic Web services. This problem becomes especially important as the current Web moves towards a Semantic Web where information technology is expected to be represented and processed by machines. Costa's probabilistic Web ontology language (PR-OWL), as extended by Carvalho (PR-OWL2), is a framework for storage of and reasoning with uncertainty information as part of the Semantic Web. This study used Costa's PR-OWL framework, as extended by Carvalho, to build an ontology that supports reasoning with service-oriented information in the context of the Semantic Web and then assessed the effectiveness of the developed ontology through the use of competency questions, as described by Gruninger and Fox and verified through the use of an automated reasoner. This research resulted in a Web Ontology Language for Services (OWL-S), PR-OWL2 based ontology, and its associated Multi-Entity Bayesian Network which are flexible and highly effective in calculating situational assessments through the propagation of posterior probabilities using Bayesian logic. Specifically, this research (1) identifies sufficient information required for effective situational awareness reasoning, (2) specifies the predicates and semantics necessary to represent service components and dependencies, (3) applies Multi-Entity Bayesian Network to reason with situational awareness information, (4) ensures the correctness and consistency of the situational awareness ontology, and (5) accurately estimates posterior probabilities consistent with situational awareness information.

Advances in ocean modeling (Barron et al., 2006) have improved such that ocean forecasts and even ensembles (e.g., Coelho et al., 2009) representing ocean uncertainty are becoming more widely available. This facilitates nowcasts (current time ocean fields / analyses) and forecasts (predicted ocean fields) of acoustic propagation conditions in the ocean which can greatly improve the planning of acoustic experiments. Modeling of acoustic transmission loss (TL) provides information about how the environment impacts acoustic performance for various systems and system configurations of interest. It is, however, very time consuming to compute acoustic propagation to and from many potential source and receiver locations for multiple locations on an area-wide grid for multiple analysis / forecast times, ensembles and scenarios of interest. Currently, to make such wide area predictions, an area is gridded and acoustic predictions for multiple directions (or radials) at each grid point for a single time period or ensemble, are computed to estimate performance on the grid. This grid generally does not consider the environment and can neglect important environmental acoustic features or can overcompute in areas of environmental acoustic isotropy. This effort develops two methods to pre-examine the area and time frame in terms of the environmental acoustics in order to prescribe an environmentally optimized computational grid that takes advantage of environmental-acoustic similarities and differences to characterize an area, time frame and ensemble with fewer acoustic model predictions and thus less computation time. Such improvement allows for a more thorough characterization of the time frame and area of interest. The first method is based on critical factors in the environment that typically indicate acoustic response, and the second method is based on a more robust full waveguide mode-based description of the environment. Results are shown for the critical factors method and show that this proves to be a viable solution for most cases studied. Limitations are at areas of high loss, which may not be of concern for exercise planning. The mode-based method is developed for range independent environments and shows significant promise for future development.

One challenge for Autonomous Underwater Vehicle (AUV) multibeam surveying is the limited ability to assess internal vertical agreement rapidly and reliably. Applying an external ellipsoid reference to AUV multibeam data would allow for field comparisons. A method is established to merge ellipsoid height (EH) data collected by a surface vessel in close proximity to the AUV. The method is demonstrated over multiple collection missions in two separate areas. Virtual tide corrector values are derived using EH data collected by a boat and a measured ellipsoid to chart datum separation distance. Those values are compared to measurements by a traditional tide gauge installed nearby. Results from the method had a mean difference of 6 centimeters with respect to conventional data and had a mean total propagated uncertainty of 15 centimeters at the 95% confidence interval. Methodologies are examined to characterize their accuracies and uncertainty contribution to overall vertical correction.

This article reviews and contrasts two approaches that water security researchers employ to advance understanding of the complexity of water-society policy challenges. A prevailing reductionist approach seeks to represent uncertainty through calculable risk, links national GDP tightly to hydro-climatological causes, and underplays diversity and politics in society. When adopted uncritically, this approach limits policy-makers to interventions that may reproduce inequalities, and that are too rigid to deal with future changes in society and climate. A second, more integrative, approach is found to address a range of uncertainties, explicitly recognise diversity in society and the environment, incorporate water resources that are less-easily controlled, and consider adaptive approaches to move beyond conventional supply-side prescriptions. The resultant policy recommendations are diverse, inclusive, and more likely to reach the marginalised in society, though they often encounter policy-uptake obstacles. The article concludes by defining a route towards more effective water security research and policy, which stresses analysis that matches the state of knowledge possessed, an expanded research agenda, and explicitly addresses inequities.