Overland flow resistance & flood generation in semi-arid environments: explaining the restrained draining of the rain in Spain

Resistance equations developed for pipe flows and open channel flows cannot be applied to model overland flows uncritically. The formulation of these equations employs several assumptions that are specific to the conditions in which they were developed and cannot be universally applied. The hydraulic behaviour of overland flow is distinct from that of pipe and channel flows and can be characterised by a high degree of variability both over space and over time as roughness elements are progressively inundated with increasing depth. A novel methodology of measuring overland flows in the field at a high- resolution permits examination of the interaction between flow variables and surface roughness. Reconstructing the water surface from elevation data and flow extent provides an estimation of the distribution of flow depths and offers a complementary perspective to more conventional approaches. Overland flows are observed to be highly variable both across and between hillslopes. The distribution of flow depths can be modelled using a two-parameter gamma distribution; both parameters show distinct variations with distance downslope and represent the progressive inundation of roughness elements with increasing depth. The flow interacts with soil surface form where it is capable of eroding its bed and the observed slope- independence of rill velocity can be explained by a feedback between flow state (as characterised by the Froude number) and surface roughness. While the existence of this interaction is affected by soil-type, the soil is observed to have little influence on the relationship between surface roughness and overland flow. Resistance is found to be spatially variable; some of this variability could be explained by the classification of areas of similar microtopogiaphy as identified in the field. This classification can be approximated by a thresholded index-based classification and provides a tool for up-scaling to the hillslope scale. Relating roughness to resistance is not straightforward. Complex natural soil surfaces vary in innumerable ways. Traditional roughness measures fall short of providing an adequate description of the complex soil surfaces observed in the field. A variety of alternative measures are developed, each of which captures a different attribute of surface form. These measures are tested to examine their influence on overland flow resistance and a suite of roughness-resistance models is developed which includes the effect of hillslope position to different degrees. Modelled flow resistance can be separated into a constant term and a depth-dependent term and can be easily incorporated into models of hillslope hydrology. This resistance is observed to decline where a hydrological connection, once established, is then maintained. Examination of the concept of hydrological connectivity in a semi-arid context suggests that the interaction between runoff generation and transfer determines not just flood peaks but also total flow amount. It is suggested that flow resistance and hence runoff transfer should be afforded the same detailed consideration as infiltration parameters, i.e. a spatially distributed and variable value (as a function of depth) that can be organised into discrete units akin to those developed for runoff generation. The parameterisation of both infiltration and resistance in this way provides a crucial interaction through the redistribution of soil moisture and runoff over hillslope surfaces. Through this mechanism, the observed complex and nonlinear runoff response to storm events may be explained as these attributes interact with rainfall characteristics and flow network development. Further understanding of this interaction could have practical implications for catchment management and affect the prioritisation of land management decisions

Monitoring and modelling hydrological response and sediment yield in a North York Moors catchment : an assessment of predictive uncertainty in a coupled hydrological-sediment yield model

A fully distributed coupled hydrological-sediment yield model was developed. An assessment was made of the predictive uncertainty in the individual model predictions, as well as the uncertainty propagated from the primary hydrological model to the secondary sediment yield model, using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. The value of additional data, in the form of additional periods of flow data, as well as deterministic (based on landuse and soil type) and random spatial parameterisation of hydrological parameters in restricting model uncertainty of the spatially lumped model parameterisation were examined, using Bayesian updating.The results revealed significant model uncertainty in both the hydrological and sediment yield models, with uncertainty bounds widest at peak flow and sediment flux, and predictive failure in recession flows, similar to other applications of GLUE methodology. Uncertainty in the sediment yield model was found to be due to uncertainty inherited from the hydrological model, as well as simplifying assumptions made about sediment removal and transport, and resulted in lower model efficiencies and generally poorer qualitative sedigraph fit.The model validation exercise revealed that the calibrated 'optimum' parameter set was not 'optimum' for all validation periods and resulted in inaccurate spatial and temporal hydrological response predictions for the validation periods. This suggested that traditional split-sample model calibration methods may not be effective in capturing the true spatial and temporal variability of the system.Successive periods of flow data were effective in reducing the calibration period uncertainty bounds. Similarly, the use of sediment yield predictions to update hydrological model uncertainty resulted in a reduction in hydrological model uncertainty. Spatially distributed parameterisation was found to also improve model predictions, resulting in a reduction in uncertainty bounds, particularly for soil-distributed parameterisation. However, stochastic parameterisation of spatially variable hydrological parameters provided equally acceptable predictions for both models, suggesting that a deterministic approach might not be required to capture the spatial variability in hydrological and sedimentological response in the study catchment, and that a stochastic approach may be adequate

Monitoring and modelling hydrological response and sediment yield in a North York Moors catchment : an assessment of predictive uncertainty in a coupled hydrological-sediment yield model

A fully distributed coupled hydrological-sediment yield model was developed. An assessment was made of the predictive uncertainty in the individual model predictions, as well as the uncertainty propagated from the primary hydrological model to the secondary sediment yield model, using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. The value of additional data, in the form of additional periods of flow data, as well as deterministic (based on landuse and soil type) and random spatial parameterisation of hydrological parameters in restricting model uncertainty of the spatially lumped model parameterisation were examined, using Bayesian updating.The results revealed significant model uncertainty in both the hydrological and sediment yield models, with uncertainty bounds widest at peak flow and sediment flux, and predictive failure in recession flows, similar to other applications of GLUE methodology. Uncertainty in the sediment yield model was found to be due to uncertainty inherited from the hydrological model, as well as simplifying assumptions made about sediment removal and transport, and resulted in lower model efficiencies and generally poorer qualitative sedigraph fit.The model validation exercise revealed that the calibrated 'optimum' parameter set was not 'optimum' for all validation periods and resulted in inaccurate spatial and temporal hydrological response predictions for the validation periods. This suggested that traditional split-sample model calibration methods may not be effective in capturing the true spatial and temporal variability of the system.Successive periods of flow data were effective in reducing the calibration period uncertainty bounds. Similarly, the use of sediment yield predictions to update hydrological model uncertainty resulted in a reduction in hydrological model uncertainty. Spatially distributed parameterisation was found to also improve model predictions, resulting in a reduction in uncertainty bounds, particularly for soil-distributed parameterisation. However, stochastic parameterisation of spatially variable hydrological parameters provided equally acceptable predictions for both models, suggesting that a deterministic approach might not be required to capture the spatial variability in hydrological and sedimentological response in the study catchment, and that a stochastic approach may be adequate

Real-time Traffic Flow Detection and Prediction Algorithm: Data-Driven Analyses on Spatio-Temporal Traffic Dynamics

Traffic flows over time and space. This spatio-temporal dependency of traffic flow should be considered and used to enhance the performance of real-time traffic detection and prediction capabilities. This characteristic has been widely studied and various applications have been developed and enhanced. During the last decade, great attention has been paid to the increases in the number of traffic data sources, the amount of data, and the data-driven analysis methods. There is still room to improve the traffic detection and prediction capabilities through studies on the emerging resources. To this end, this dissertation presents a series of studies on real-time traffic operation for highway facilities focusing on detection and prediction.First, a spatio-temporal traffic data imputation approach was studied to exploit multi-source data. Different types of kriging methods were evaluated to utilize the spatio-temporal characteristic of traffic data with respect to two factors, including missing patterns and use of secondary data. Second, a short-term traffic speed prediction algorithm was proposed that provides accurate prediction results and is scalable for a large road network analysis in real time. The proposed algorithm consists of a data dimension reduction module and a nonparametric multivariate time-series analysis module. Third, a real-time traffic queue detection algorithm was developed based on traffic fundamentals combined with a statistical pattern recognition procedure. This algorithm was designed to detect dynamic queueing conditions in a spatio-temporal domain rather than detect a queue and congestion directly from traffic flow variables. The algorithm was evaluated by using various real congested traffic flow data. Lastly, gray areas in a decision-making process based on quantifiable measures were addressed to cope with uncertainties in modeling outputs. For intersection control type selection, the gray areas were identified and visualized

A Bayesian Network-based Decision Framework for Selecting Project Delivery Methods in Highway Construction

Transportation agencies currently have several options in delivering their highway construction projects. Selecting an appropriate project delivery method (PDM) is a complex decision-making process. Researchers and transportation industry practitioners have been striving to discover the knowledge and methodologies to enhance the project delivery decision. However, through conducting an extensive literature review of existing methodologies, it is found that quantitative approaches, implementing probabilistic comparisons, to project delivery decisions are not fully addressed or understood. To fill this gap, this research aims at developing a decision framework by implementing Bayesian Network (BN), an advanced statistical tool, for selecting an appropriate PDM in highway construction industry. The BN-based decision framework incorporates the decision driving factors such as project attributes, risk profiles, project complexity, cost, and time. In developing the BN-based decision framework, this dissertation employed several research methodologies and techniques, including content analysis, questionnaire, case studies, cluster analysis, ANOVA, correlation and reliability analysis, and cross-validation techniques. The dissertation follows a four-journal paper format. The first paper explores the impact of project size on highway design-bid-build (D-B-B) and design-build (D-B) projects. The second paper identifies and evaluates the risks involved in highway project delivery methods: D-B-B, D-B, and construction manager/general contractor (CM/GC). Building upon the findings and results from the first two papers, the third paper determines the probabilistic dependence between the decision factors and develops a theoretical decision framework using BNs for selecting an appropriate PDM. The fourth paper focuses on demonstrating the practical application of the proposed BN-based decision framework using case studies. Also, the final paper presents a k-fold (cross-validation) technique to test and verify the accuracy of the proposed BN-based decision framework. This dissertation contributes to the theoretical body of knowledge by introducing a new quantitative approach using BNs for PDM selection. The findings from this study indicate that implementing BNs facilitate the owner/decision maker in a better understanding of probabilistic comparison and selection of an appropriate PDM for highway construction projects. State transportation agency officials can utilize these findings as a supplemental tool for their project delivery decisions

Biomarker for tracking progression of Alzheimer's disease in clinical trials

Currently, there are no treatments available for mitigating the neurological effects of Alzheimer's disease. All clinical trials of disease-modifying treatments, which showed promise in animal models, have failed to show a significant treatment effect in human trials. The lack of a sensitive outcome measure and the focus on the dementia stage for investigating treatments are believed to be the primary reasons behind the failure of all clinical trials till date. The currently used outcome measure, the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog), suffers from low sensitivity in tracking progression of cognitive impairment in clinical trials. A shift in the focus to the prodromal mild cognitive impairment (MCI) stage may help improve the efficiency of clinical trials. However, even lower sensitivity of the ADAS-Cog and an inability to specifically select progressive MCI patients limit the efficiency of clinical trials in the MCI stage. Cerebral atrophy measured on structural magnetic resonance (MR) imaging is highly promising for tracking disease progression in clinical trials. However, cerebral atrophy has not been yet approved as a valid biomarker due to the lack of an understanding behind its relationship with cognitive impairment. The focus of this dissertation spans across the two research areas of (i) developing automatic algorithms for analysis of patients' brain MR volumes, and (ii) improving the efficiency of clinical trials of disease-modifying treatments. This dissertation presents a novel knowledge-driven decision theory approach for automatic tissue segmentation of brain MR volumes, which shows better segmentation performance than the existing approaches. The remaining dissertation contributions focus at improving the efficiency of clinical trials of disease-modifying treatments. An improved scoring methodology is presented for the ADAS-Cog outcome measure, which measures cognitive impairment with better accuracy and significantly improves the sensitivity of the ADAS-Cog in the mild-to-moderate Alzheimer's disease stage. However, the ADAS-Cog continues to suffers from low sensitivity in the MCI stage due to inherent limitations of its items. For improving the efficiency of clinical trials in the MCI stage, a biomarker has been developed that combines the ADAS-Cog with cerebral atrophy for more accurate tracking of Alzheimer's progression and facilitating selection of MCI patients in clinical trials.Biomedical Engineerin

Decision Support Model For Construction Crew Reassignments

The reassignment of crews on a construction project in response to changes occurs on a frequent basis. The factors that affect the crew reassignment decision can be myriad and most are not known with certainty. This research addresses the need for a decision support model to assist construction managers with the crew reassignment problem. The model design makes use of certainty factors in a decision tree structure. The research helped to determine the elements in the decision tree, the appropriate combination rules to use with the certainty factors, and the method for combining the certainty factors and costs to develop a measure of cost for each decision option. The research employed surveys, group meetings, and individual interviews of experienced construction managers and superintendents to investigate the current methods used by decision makers to identify and evaluate the key elements of the construction crew reassignment decision. The initial research indicated that the use of certainty factors was preferred over probabilities for representing the uncertainties. Since certainty factors have not been used in a traditional decision tree context, a contribution of the research is the development and testing of techniques for combining certainty factors, durations, and costs in order to represent the uncertainty and to emulate the decision process of the experts interviewed. The developed model provides the decision maker with an estimate of upper and lower bounds of costs for each crew reassignment option. The model was applied contemporaneously to six changes on three ongoing construction projects to test the model and assess its usefulness. The model provides a previously unavailable tool for the prospective identification and estimation of productivity losses and potential costs that emanate from changes. The users indicated the model process resulted in concise and complete compilations of the elements of the crew reassignment decision and that the model outputs were consistent with the users\u27 expectations

The Role Of Working Memory In Implementing Computational Elements Of Visuo-Spatial Decision-Making

Decision making is a dynamic process by which a person integrates sensory evidence with prior expectations to select an action to achieve a desired outcome. Often, this process requires working memory to hold and manipulate the relevant information. Working memory has several known limitations and models of implementation that have not been widely considered in the context of decision-making. For decisions regarding spatial stimuli, the dorsolateral prefrontal cortex (dlPFC) is likely to be essential. Activity in this area has been shown to relate to both decision making and working memory, and has led to concrete models of how spatial information may be stored by population activity. To investigate how this activity might be leveraged to implement several computational elements of decision making, we performed two related experiments.In the first, we had human participants perform a working memory task that required the reporting of a decision variable (average location) to determine how working memory limitations impacted decision precision. We also used models of working memory to predict and interpret what information was being actively maintained. We report not only the novel finding that decision variables held in working memory lose precision over time but also that the degree of precision loss depends on the strategy used to make the decision, which differed across participants and conditions. In our second study, we trained monkeys to perform an adaptive oculomotor delayed response task in which they had to integrate cue information with context to select the target most likely to be rewarded. The goal of this study was to investigate whether the tuning properties of working-memory related dlPFC neurons adjusted according to the statistics of the task, corresponding to adaptive behavior. We found that not all monkeys display adaptive behavior, but developed method for interrogating the dynamics of neural responses in those that do. This body of work contributes to our understanding of how working memory may implement the representation of a decision variable or prior information used to interpret incoming evidence. Such understanding may ultimately lead to more effective approaches to addressing disorders of maladaptive decisions, such as addiction and PTSD

The Perception of Surface Properties: Translucence and Gloss

The human visual system is sensitive to differences in gloss and translucence, two optical properties which are found in conjunction in many natural materials. They are driven by similar underlying physical properties of light transport - the degree to which light is scattered from the surface of a material, or within the material. This thesis aimed to address some fundamental questions about how gloss and translucence are perceived. Two psychophysical methods (maximum likelihood difference scaling, and conjoint measurement) were used throughout, as they provided an appropriate way of investigating how perceptual experiences related to physical variables. In the introduction, I review the literature on the perception of gloss and translucence. Study 1 investigated the relationship between variables controlling light transport in translucent volumes and percepts of translucence. The results show that translucence perception is not based on estimates of light transport properties per se, but probably uses spatially-related statistical pseudocues in conjunction with other cues. Study 2 examined a similar issue, but the translucent material was presented as a layer enveloping a solid object. Behavioural responses were similar for these translucent materials, which were perceived as glossy layers of coating. Study 3 further explored established findings that perceived translucence shows inconstancy under changes in viewing condition. Perceived translucence was dependent in a complex way on both light-scattering in the material and illumination direction in both volumes and layers of translucent materials. Study 4 used similar layers of subsurface light-scattering and -absorbing material and applied them to multiple base materials. Opacity and a lack of mirror-like reflections enabled observers to make the most accurate independent judgements of darkness and cloudiness. Study 5 explored observers' sensitivity to spatial variation of scatter across a surface using similar layers of coating, and the way in which observers might weight cues differently to answer subtly different questions (judgements of 'shininess' vs. 'cleanliness'). Layer thickness and variation of scatter significantly affected perceived shine and cleanliness, with layer thickness influencing decisions more than variation. Scatter variation contributed to decisions significantly more for judgements of cleanliness than shine. Study 6 investigated how tactile surface roughness influenced perceived gloss. Previous findings have shown that tactile compliance and friction influence perceived gloss, and that friction interacts with visual gloss. Our results showed that surface roughness and visual gloss both affected perceived gloss, but there was no interaction, suggesting that different types of haptic information are combined with visual information differently. Finally, study 7 explored the potential cortical basis of perceived translucence. Through testing a neuropsychological patient, we showed that perceived translucence is dependent on cortical areas not responsible for colour or texture discrimination. The thesis concludes with a discussion of additional recent findings, the implications of the research reported in this thesis, and proposals for future research