Thesis (Ph. D.)--Oregon State University, 2005. / Printout. Includes map in pocket. Includes bibliographical references. Also available on the World Wide Web.

Thesis--University of Wisconsin--Madison. / Typescript (Carbon copy). eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 253-259).

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 109-110). Also available on the Internet.

Åar är en vattenresurs som är starkt beroende av den omgivande landmiljön för att fungera väl. Liksom andra vattenresurser betraktas de idag inte enbart som en resurs, utan har även konstaterats vara ett arv som måste skyddas och försvaras. För att möjliggöra detta krävs metoder för att kunna bedöma åars tillstånd och kommunicera resultaten till beslutsfattare och intressenter, för att på så sätt skapa en god vattenförvaltning. Metoderna behöver inte enbart inkludera ån i sig, utan även innefatta den omgivande landmiljön då dessa är beroende av varandra. Metoden Landscape Character Assessment går ut på att dela in landskapet i landskapskaraktärsområden och landskapskaraktärstyper. Genom att klassificera landskapet och dess karaktär möjliggör Landscape Character Assessment bedömningar av olika slag, men har tidigare inte utvecklats för att inkludera åar. I denna uppsats utvecklas därför ett nytt klassificeringssystem för användning av Landscape Character Assessment avsett för åar, som appliceras på Fibyån utanför Uppsala. Genom detta undersöks hur ån kan bedömas och planeras ur ett vattenförvaltningsperspektiv.  Klassificeringen av Fibyån diskuteras utifrån en lista över miljöproblem och påverkanskällor som identifierats för det vattendistrikt vari Uppsala ingår. Resultatet visar på tydliga kopplingar mellan visuella attribut i åns olika karaktärsområden och de miljöproblem som präglar området, samt visar på potential för användning av metoden för framtida planering och förvaltning av Fibyån.

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Phenological events in temperate forests, such as bud burst and senescence, exert strong control over seasonal fluxes of water, energy and carbon. The timing of these transitions is influenced primarily by air temperature and photoperiod, although the exact nature and magnitude of these controls is poorly understood. In this dissertation, I use in situ and remotely sensed observations of phenology in combination with surface meteorological data and measurements of biosphere-atmosphere carbon exchanges to improve understanding and develop models of canopy phenology in temperate forest ecosystems. In the first element of this research I use surface air temperatures and eddy covariance measurements of carbon dioxide fluxes to evaluate and refine widely used approaches for predicting the onset of photosynthesis in spring that account for geographic variation in thermal and photoperiod constraints on phenology. Results from this analysis show that the refined models predict the onset of spring photosynthetic activity with significantly higher accuracy than existing models. A key challenge in developing and testing these models, however, is lack of adequate data sets that characterize phenology over large areas at multi-decadal time scales. To address this need, I develop a new method for estimating long-term average and interannual dynamics in the phenology of temperate forests using time series of Landsat TM/ETM+ images. Results show that estimated spring and autumn transition dates agree closely with in-situ measurements and that Landsat-derived estimates for the start and end of the growing season in Southern New England varied by as much as 4 weeks over the 30-year record of Landsat images. In the final element of this dissertation, I use meteorological data, species composition maps, satellite remote sensing, and ground observations to develop models of springtime leaf onset in temperate deciduous forests that account for geographic differences in how forest communities respond to springtime climate forcing. Results demonstrate important differences in cumulative heating requirements and photoperiod cues among forest types and that regional differences in species composition explain substantial geographic variation in springtime phenology of temperate forests. Together, the results from this dissertation provide an improved basis for observing and modeling springtime phenology in temperate forests.

This thesis examines the dynamic of infectious diseases after natural disasters. Methods from epidemiology and geography intersect in the nexus of this research to form new insights into the risk of infectious disease in the aftermath of natural disaster and catastrophe. In the past decades, natural disasters have increased in frequency and magnitude, and with climate change progressing as it is, this trend is expected to continue. It is thus important to gain a fuller understanding of the dynamic between natural disaster and disease, and challenge the persisting problems in disaster and disease response efforts. Two approaches were taken to determine the risk of disease after disaster. Firstly, by pooling data from previously published literature, a form of meta-analysis was conducted to gain insight into risk patterns as well as to define relevant confounding factors that held significance in determining vulnerabilities of affected populations. For this analysis, a new tool was applied to identify relevant research, and this tool is expected to be useful in future study of the subject. Secondly, a set of empirical studies were conducted to determine the association between types of natural disasters, geographic region, and four distinct disease profiles. Cholera, malaria, tuberculosis, and the co-infection with HIV and tuberculosis served as examples for the types of diseases commonly observed after disasters (diarrhoeal diseases, vector-borne diseases, and acute respiratory infections). Logistic regression models were used to find the odds ratios for above average diseases at different tiers of disaster magnitude. It was shown in this research that the relative risk of infectious disease after natural disasters was 3.45, indicating a higher probability of disease after disasters. Specific results show that disasters affecting higher numbers of the population typically lead to increases in new infections. Most interestingly, tuberculosis relapses showed significant increases after natural disasters, especially meteorological and hydrological disasters.

Geomorphological evidence has frequently been used to infer past environmental conditions, but in recent years the emergence of landscape evolution models (LEMs) has opened the possibility of us- ing numerical modelling as a tool in palaeo-environmental reconstruction. The application of LEMs for this purpose involves retrodictive modelling, each simulation scenario being congured with model variables (e.g. reflecting climate change) and parameters to reflect a specic hypothesis of environmental change. Plausible scenarios are then identied by matching contemporary observed and modelled landscapes. However, although considerable uncertainty is known to surround the specication of model driving conditions and parameters, previous studies have not considered this issue. This research applies a technique of accounting for the uncertainty surrounding the speci- cation of driving conditions and model parameters by using reduced complexity 'metamodels' to analyse the full model parameter space and thus constrain sources of uncertainty and plausible retrodicted scenarios more eectively. This study applies the developed techniques to a case study focused on a specic set of coastal gullies found on the Isle of Wight, UK. A key factor in the evolution of these gullies are the relative balance between rates of cliff retreat (which reduces gully extent) and headwards incision caused by knickpoint migration (which increases gully extent). To inform the choice and parameterisation of the numerical model used in this research an empirical- conceptual model of gully evolution was initially developed. To provide a long-term context for the evolution of the gullies and to identify the relative importance of the various driving factors, the Holocene erosional history of the Isle of Wight gullies was then simulated using a LEM. In a preliminary set of simulations a 'traditional' (i.e. with no consideration of parameter uncertainty) retrodictive modelling approach was applied, in which driving variables were arbitrarily altered and observed and simulated landscape topographies compared, under various scenarios of imposed environmental change. These initial results revealed that the coastal gullies have been ephemeral in nature for much of the Holocene, only becoming semi-permanent once cli retreat rates fall below a critical threshold at 2500 cal. years BP. Next, in an attempt to constrain more detailed erosional histories and to explore the extent to which retrodicted interpretations of landscape change were confounded by uncertainty, a Central Composite Design (CCD) sampling technique was employed to sample variations in the model driving variables, enabling the trajectories of gully response to dierent combinations of the driving conditions to be modelled explicitly. In some of these simulations, where the ranges of bedrock erodibility (0:03-0:04m0:2a).

The aim of this thesis is to develop an integrated methodology for investigating the potential for new local railway stations within a given area, with particular emphasis on the use of Geographical Information Systems (GIS). Existing methods for assessing the case for constructing new local railway stations have often been found wanting, with the forecasts produced proving to be inaccurate. A review of previous work in this field has been undertaken and methodologies with the potential to enhance local rail demand models have been identified. Trip rate and trip end models have been developed which are capable of forecasting usage at new station sites anywhere in England and Wales. Geographically Weighted Regression (GWR) has been used to enhance the performance of these models and to account for local variations in the effects of explanatory variables on rail demand. Flow level models have been produced for stations in South-East Wales, with a range of model formulations tested. A survey of ultimate passenger trip origins and destinations was carried out in the same area, enabling the accuracy of theoretical station catchment definition methods to be tested. A GIS-based procedure for locating potential sites for new railway stations within a given area has been developed. This was combined with the results from the demand models and estimates of associated costs and benefits to give a synthesised appraisal procedure capable of assessing the case for constructing particular stations. This procedure was applied to 14 sites in South-East Wales and, along with trip end forecasts for 421 sites across the country, this indicated that there is almost certainly a positive case for constructing a significant number of new railway stations in the UK.

Low-lying coastal zones are high-risk areas threatened by flooding due to extreme coastal events and rising sea-levels. The coastal floodplain system includes elements such as near-shore waves and water levels, inter-tidal beaches and coastal habitats, natural and artificial sea defences and multiple inland floodplain features. Flood risk studies generally achieve an integrated assessment of these elements using multiple numerical models for different floodplain elements. However fundamental choices of floodplain description and the appropriate data, methods and models can vary widely between different sites and flood risk studies. A comprehensive conceptual model is needed to describe the floodplain system and help inform these choices in each site. However a descriptive conceptual model for coastal floodplain systems does not exist at present. There is a bias in flood risk studies towards the direct use of numerical models with limited use of conceptual models – existing models are implicit and do not describe the coastal floodplain system. This thesis addresses this gap by developing, applying and testing a rapid appraisal tool that conceptually describes the coastal floodplain as a system of interacting elements. The tool is developed in two parts – i) a quasi-2D Source – Pathway – Receptor (SPR) model that provides a comprehensive qualitative description of the floodplain; and ii) a Bayesian network model that uses this description to quantify individual elements as sources, pathways and receptors of flood propagation. The quasi-2D SPR is applied in 8 diverse coastal zones across Europe 4 of which include nested case-studies. It is an effective way of gathering and describing information about the floodplain from stakeholders across multiple disciplines. The Bayesian network model is applied to two contrasting floodplain systems in England – Teignmouth and Portsmouth. The network model is effective in pinpointing critical flood pathways and identifying key knowledge gaps for further analyses. The two models together provide a comprehensive understanding of the coastal floodplain system that can be used to inform and target the use of more detailed numerical models. Hence this thesis provides a conceptual model and tool to improve flood risk assessment. It makes conceptual understanding of the floodplain explicit and stratifies quantitative analysis by application of a rapid assessment tool before the use of detailed numerical models.