The complex interaction between flow, vegetation and sediment drives the never settled changes of riverine system. Vegetation intercepts rainfall, adds resistance to surface flow, and facilitates infiltration. The magnitude and timing of flood flow are closely related to the watershed vegetation coverage. In the meantime, flood flow can transport a large amount of sediment resulting in bank erosion, channel degradation, and channel pattern change. As climate changes, future flood frequency will change with more intense rainfalls. However, the quantitative simulation of flood flow in vegetated channel and the influence of climate change on flood frequency, especially for the arid and semi-arid Southwest, remain challenges to engineers and scientists. Therefore, this research consists of two main parts: simulate unsteady flow and sediment transport in vegetated channel network, and quantify the impacts of climate change on flood frequency. A one-dimensional model for simulating flood routing and sediment transport over mobile alluvium in a vegetated channel network was developed. The modified St. Venant equations together with the governing equations for suspended sediment and bed load transport were solved simultaneously to obtain flow properties and sediment transport rate. The Godunov-type finite volume method is employed to discretize the governing equations. Then, the Exner equation was solved for bed elevation change. Since sediment transport is non-equilibrium when bed is degrading or aggrading, a recovery coefficient for suspended sediment and an adaptation length for bed load transport were used to quantify the differences between equilibrium and non-equilibrium sediment transport rate. The influence of vegetation on floodplain and main channel was accounted for by adjusting resistance terms in the momentum equations for flow field. A procedure to separate the grain resistance from the total resistance was proposed and implemented to calculate sediment transport rate. The model was tested by a flume experiment case and an unprecedented flood event occurred in the Santa Cruz River, Tucson, Arizona, in July 2006. Simulated results of flow discharge and bed elevation changes showed satisfactory agreements with the measurements. The impacts of vegetation density on sediment transport and significance of non-equilibrium sediment transport model were accounted for by the model. The two-dimensional surface flow model, called CHRE2D, was improved by considering the vegetation influence and then applied to Santa Cruz River Watershed (SCRW) in the Southern Arizona. The parameters in the CHRE2D model were calibrated by using the rainfall event in July 15th, 1999. Hourly precipitation data from a Regional Climate Model (RCM) called Weather Research and Forecasting model (WRF), for three periods, 1990-2000, 2031-2040 and 2071-2079, were used to quantify the impact of climate change on the magnitude and frequency of flood for the Santa Cruz River Watershed (SCRW) in the Southern Arizona. Precipitation outputs from RCM-WRF model were bias-corrected using observed gridded precipitation data for three periods before directly used in the watershed model. The watershed model was calibrated using the rainfall event in July 15th, 1999. The calibrated watershed model was applied to SCRW to simulate surface flow routing for the selected three periods. Simulated annual and daily maximum discharges are analyzed to obtain future flood frequency curves. Results indicate that flood discharges for different return periods are increased: the discharges of 100-year and 200-year return period are increased by 3,000 and 5,000 cfs, respectively.

Reactive transport models are essential tools for predicting contaminant fate and transport in the subsurface and for designing effective remediation strategies. Sound understanding of subsurface mixing in heterogeneous porous media is the key for the realistic modeling of reactive transport. This dissertation aims to investigate the extent of mixing and improve upscaling effective macroscopic models for mixing-controlled reactive transport in connected heterogeneous formations, which usually exhibit strongly anomalous transport behavior. In this research, a novel approach is developed for an accurate geostatistical characterization of connected heterogeneous formations transformed from Gaussian random fields. Numerical experiments are conducted in such heterogeneous fields with different connectivity to investigate the performance of macroscopic mean transport models for simulating mixing-controlled reactive transport. Results show that good characterization of anomalous transport of a conservative tracer does not necessarily mean that the models may characterize mixing well and that, consequently, it is questionable that the models capable of characterizing anomalous transport behavior of a conservative tracer are appropriate for simulating mixing-controlled reactive transport. In connected heterogeneous fields with large hydraulic conductivity variances, macroscopic mean models ignoring concentration variations yield good prediction, while in fields with intermediate conductivity variances, the models must consider both the mean concentration and concentration variations, which are very difficult to evaluate both theoretically and experimentally. An innovative and practical approach is developed by combining mean conservative and reactive breakthrough curves for estimating concentration variations, which can be subsequently used by variance transport models for prediction. Furthermore, a new macroscopic framework based on the dual-permeability conceptualization is developed for describing both mean and concentration variation for mixing-controlled reactive transport. The developed approach and models are validated by numerical and laboratory visualization experiments. In particular, the new dual-permeability model demonstrates significant improvement for simulating mixing-controlled reactive transport in heterogeneous media with intermediate conductivity variances. Overall, results, approaches and models from this dissertation advance the understanding of subsurface mixing in anomalous transport and significantly improve the predictive ability for modeling mixing-controlled reactive transport in connected heterogeneous media.

Over the last few decades there has been a gradual transformation in both the spatial and temporal patterns of urban activities. The percentage share of non-discretionary travel such as morning rush-hour commuting has been declining with the increased income level. Discretionary activities appear to rise prominently in urban business and leisure hubs, attracting large volumes of crowds which in turn imply new and changed demand for building floorspace and urban infrastructure. Despite impressive advances in the theories and models of infrastructure demand forecasting, there appear to be an apparent research gap in addressing the practical needs of infrastructure planning in and around those growing urban activity hubs. First, land use and transport interaction models which have to date been the mainstay of practical policy analytics tend to focus on non-discretionary activities such as rush-hour commuting. Secondly, the emerging activity based models, while providing significant new insights into personal, familial activities, especially the discretionary travel, are so data hungry and computing intensive that they have not yet found their roles in practical policy applications. This dissertation builds on the insights from above schools of modelling to develop a new approach that addresses the infrastructure planning needs of the growing urban hubs while keeping the data and computing realistic in medium to high income cities. The new model is designed based on an overarching hypothesis that considerable efficiency and welfare gains can be achieved in the planning and development of urban business and leisure hubs if the infrastructure provisions for discretionary and non-discretionary activities can be coordinated. This is a research theme that has been little explored in current literature. The new infrastructure demand forecasting model has been designed with regard to the above hypothesis and realistic data availability, including those emerging online. The model extends the framework of land use transport interaction models and aim to provide a practical modelling tool. Land use changes are accounted for when testing new infrastructure investment initiatives and especially the road and public transport loads are assessed throughout all time periods of a working day. The new contribution to the modelling methodology includes the extension to the land use transport interaction framework, the use of social media data for estimating night market activity distribution and a rapid estimation of road traffic speeds from Google directions API, and model validation. Another new contribution is the understanding of the nature and magnitude of future infrastructure demand through assessing three alternative land use scenarios: (1) business as usual, (2) inner city regeneration for a major business hub around the night market, and (3) dispersed suburban growth with distant subcentres. The model is able to assess the implications for future infrastructure demand and user welfare through discerning the distinct discretionary and non-discretionary activity patterns.

abstract: Institutions of higher education, particularly those with large student enrollments, constitute special generators that contribute in a variety of ways to the travel demand in a region. Despite the importance of university population travel characteristics in understanding and modeling activity-travel patterns and mode choice behavior in a region, such populations remain under-studied. As metropolitan planning organizations continue to improve their regional travel models by incorporating processes and parameters specific to major regional special generators, university population travel characteristics need to be measured and special submodels that capture their behavior need to be developed. The research presented herein begins by documenting the design and administration of a comprehensive university student online travel and mode use survey that was administered at Arizona State University (ASU) in the Greater Phoenix region of Arizona. The dissertation research offers a detailed statistical analysis of student travel behavior for different student market segments. A framework is then presented for incorporating university student travel into a regional travel demand model. The application of the framework to the ASU student population is documented in detail. A comprehensive university student submodel was estimated and calibrated for integration with the full regional travel model system. Finally, student attitudes toward travel are analyzed and used as explanatory factors in multinomial logit models of mode choice. This analysis presents an examination of the extent to which attitudes play a role in explaining mode choice behavior of university students in an urban setting. The research provides evidence that student travel patterns vary substantially from those of the rest of the population, and should therefore be considered separately when forecasting travel demand and formulating transport policy in areas where universities are major contributors to regional travel. / Dissertation/Thesis / Doctoral Dissertation Civil Engineering 2014

Air quality degradation is emerging to be an issue of major concern in India. Recent investigations have shown that anthropogenic aerosols have significant impact on climate as well as on health. In fourth assessment report of IPCC, it has been mentioned that radiative effects of anthropogenic aerosols constitute one of the major uncertainties in assessing aerosol-induced climate impact. In addition to climate impacts, aerosol causes respiratory and cardiovascular diseases, air quality degradation, acidification of aquatic and terrestrial ecosystems. Characterization of anthropogenic aerosol fraction (defined as the fraction of anthropogenic aerosols to composite aerosols) is an appealing topic of research in current scenario. The first step towards achieving this goal is to separate natural aerosol from composite aerosols, which is a complex task. The main objective of this thesis work is the assessment of air quality and anthropogenic aerosol fraction over India using observations (ground-based as well as satellite-based) and chemistry-transport model. Specifically objectives are (a) assessment of air quality and anthropogenic aerosol fraction over Indian region (b) develop a method to derive natural aerosol properties over land and oceans using multi-satellite data analysis, which is first step towards separating natural aerosol effects from its anthropogenic counter parts and (c) evaluate performance of CHIMERE chemistry-transport model for Indian region and validate its suitability to air quality studies over India. In this thesis, different approaches have been followed such as ground-based observations, multi-satellite data analysis and CHIMERE transport model. We have used multi-year observations of particulate mass (PM) concentration, aerosol black carbon (BC) mass concentration and aerosol optical depth (AOD) from a network of observatories to make an assessment of ambient air quality over India. First, we have developed a method to estimate dust and sea-salt optical depth using multi-satellite data analysis. This enabled the determination of anthropogenic aerosol fraction over land and ocean and we have validated this method by comparing against observations. Surprisingly, even over desert locations in India and Saudi Arabia, the anthropogenic fraction were unexpectedly high (~0.3 to 0.4) and the regionally averaged anthropogenic fraction over India was 0.620.06 (for the year 2004). The CHIMERE chemistry-transport model was used to simulate PM, BC and AOD over India and are compared with measurements. Evaluation of CHIMERE output shows that diurnal and seasonal trends are captured reasonably well by the model. It was found that absolute magnitudes differ substantially during monsoon months. Model simulations are also used to estimate anthropogenic fraction over Indian region and are compared with observations. Implications of the results are discussed. Mineral dust constitutes the single largest contributor of natural aerosols over continents. The first step towards separating natural aerosol radiative impact from its anthropogenic counterparts over continents is to gather information on dust aerosols. The infrared (IR) radiance (10.5–12.5 mm) acquired from the Kalpana satellite (8-km resolution) was used to retrieve regional characteristics of dust aerosols over the Afro-Asian region during the winter of 2004, coinciding with a national aerosol campaign. Here, we used aerosol-induced IR radiance depression as an index of dust load. The regional distribution of dust over various arid and semi-arid regions of India and adjacent continents has been estimated, and these data in conjunction with regional maps of column aerosol optical depth (AOD) are used to infer anthropogenic aerosol fraction. Surprisingly, even over desert locations in India and Saudi Arabia, the anthropogenic fraction were relatively high (0.3 to 0.4) and the regionally averaged anthropogenic fraction over India was 0.62 ±0.06. Sea-salt constitutes the single largest contributor of natural aerosols over oceans. We derive sea-salt aerosol distribution using a method utilizing multi-satellite data analysis. This information was used in conjunction with dust aerosols retrieval to calculate anthropogenic fraction over land and ocean. First, we derived a relation between MODIS AOD and NCEP wind speed at the sea-surface. An exponential increase in AOD as a function of wind speed was observed from mid of southern ocean to northern Arabian Sea. Latitudinal variation of wind independent component of optical depth (τ0) and wind index (b) was used to estimate the sea-salt optical depth over Arabian Sea. The value of τ0 showed an exponential increase as we move towards north from 35°S while b showed linear increase. The derived relations for the τ0 and b have been used to derive the sea-salt AOD distribution over oceanic regions in the domain (Eq-30°N; 30°E-110°E). Then we subtract the natural aerosol contribution from composite AOD data from MISR to obtain anthropogenic aerosol fraction. Over Indian region, high anthropogenic fraction was observed over northern belt specifically Indo-Gangetic Plains (IGP). Annually averaged anthropogenic fraction over Indian domain (4N-29.5N; 67E-88.5E) is ~0.43. Further, we have investigated the impact of sea-surface winds on sea-salt radiative effect in visible and infrared region with the help of SBDART radiative transfer model. The SBDART simulations have shown that at 15 m s-1, sea-salt induced shortwave cooling at the sea-surface was -86 W m-2. Derivation of anthropogenic aerosol fraction over whole Indian domain has demonstrated the importance of anthropogenic aerosols. This observation motivated us to examine the air quality over Bangalore, a fast growing city in India. We have analyzed data from ground based measurements of particulate matter, observations from satellites and also model simulations. Comparison with national threshold indicates that more than 50% of observations were above the residential threshold. To represent the air quality of Bangalore we have calculated the air quality index (AQI) for air pollutants. Coarse spatial and temporal resolution of observational data is one major shortcoming in such analysis. Therefore, satellite observations are alternative to quantify the air quality over large area. We have used MODIS AOD and RSPM to develop an empirical relation between these two parameters. A reasonably good agreement was observed between measured RSPM and RSPM derived using satellite data (by applying empirical relation). The CHIMERE chemistry-transport model was used to simulate PM, BC and AOD over India and are compared with measurements. Evaluation of CHIMERE output shows that diurnal and seasonal trends are captured reasonably well by the model. It was found that absolute magnitudes differ substantially during pre-monsoon and monsoon months. Model simulations are also used to estimate anthropogenic fraction over Indian region and are compared with observations. Implications of the results and future scope are discussed. The validation of model results suggests that CHIMERE model is suitable for simulating air quality over India with reasonable accuracy. This would in turn help us to address the impacts of air pollution on regional climate and help policy makers in order to reduce the air pollution. In summary, we have developed a new method to infer natural aerosol (sea-salt and dust) properties using multi-satellite data analysis. This technique has been applied to derive anthropogenic aerosol fraction over Indian region. Surprisingly, even over desert locations in India and Saudi Arabia, the anthropogenic fraction were relatively high (0.3 to 0.4) and regionally averaged anthropogenic fraction over India was 0.62±0.06 in 2004. This study indicates that multi-satellite observations can provide a powerful tool in monitoring air quality. We have noticed that anthropogenic fraction was 0.62 in 2004 and reduced to 0.43 in 2008. Major anthropogenic aerosol over India is BC and decreasing trend in BC could be one of the reasons for the decrease in anthropogenic fraction from 2004 to 2008. The CHIMERE chemistry-transport model was used to simulate PM, BC and AOD over India and are compared with measurements. Evaluation of CHIMERE output shows that diurnal and seasonal trends are captured reasonably well by the model. It was found that absolute magnitudes differ substantially during pre-monsoon and monsoon months. Presence of elevated aerosol layers during these seasons could be one of the sources for such discrepancy. Model simulations of anthropogenic fraction over Indian region are compared with observations and found good agreement. Results from this thesis moves us one step forward to reduce the uncertainties involved in anthropogenic aerosol fraction, its spatial and temporal distributions and regional distribution of OC/BC ratio, which are most important parameters in order to assess the climate forcing by anthropogenic aerosols.

Dlouhodobé vdechování částic může přispívat ke vzniku nebo zhoršení nejrůznějších plicních onemocnění. Na druhou stranu, vdechování léčiv je často používanou metodou podávání léků proti astmatu a jiným nemocem dýchacího ústrojí. V obou případech je důležité dobře porozumět mechanismům, na jejichž základě funguje pohyb částic a jejich usazování v dýchacích cestách. Cílem této disertační práce bylo získat nová experimentální data depozice částic a analyzovat vliv tvaru částic a průtoku vzduchu na depozici. Byla studována depozice porézních a vláknitých částic v realistickém modelu dýchacích cest. Porézní částice byly vyrobeny různými metodami, např. sprejovým sušením nebo metodou krystalizace. Takto vyrobené částice byly použity při depozičních experimentech. Detekce částic byla provedena pomocí UV/VIS spektrofotometrie. Vláknitý aerosol byl vytvořen rozdrcením skelné vaty. Takto vzniklé vláknité částice byly několikrát prosety přes řadu sít a dále roztříděny podle délky pomocí klasifikátoru pracujícím na principu dielektroforézy. Následná depozice byla vyhodnocena použitím mikroskopie s fázovým kontrastem. Ke zrychlení analýzy byl vyvinut program, který dokáže na základě analýzy obrazu najít a spočítat vlákna. Výsledky experimentu byly použity k určení depozičních charakteristik. S jejich pomocí pak byl kvantifikován vliv tvaru částic a průtoku na míru usazování. Depoziční účinnost částic rostla v závislosti na Stokesově čísle, což poukazuje na vliv setrvačnosti při usazování částic. Bylo prokázáno, že depoziční účinnost porézních částic je podobná té u částic kulových při stejném Stokesově čísle. Vláknité částice se usazovali méně efektivně v porovnání s kulovými částicemi majícími stejné Stokesovo číslo. Jelikož byly okrajové podmínky dostatečně popsány a model plic je k dispozici i v digitální podobě, je možné data použít k validaci výsledků numerických simulací.

The subject of this diploma thesis is study of the impacts of different implementations of turbulence and convection in the Regional Climate Model (RegCM, version 4.6) on the ability to predict the chemical pollution of the troposphere in Europe, which was simulated by the chemical transport model CAMx (Comprehensive Air quality Model with extensions, version 6.50) driven by RegCM meteorology using offline coupling of theese two models. Thesis also includes a brief description of the tropospheric chemistry, a discussion of the influence of meteorological conditions on changes in pollutant concentrations in the troposphere, a brief introduction to the regional climate modeling, a description of both models and method of their coupling. The main focus of the thesis is the analysis of the impacts of different parameterizations in RegCM model simulations on some climatic elements, validation of air temperature and precipitation, analysis of the impacts of changes in driving meteorology on ozone, nitrogen dioxide, sulfur dioxide and fine aerosol, validation of model concentrations of these pollutants with measurements at rural monitoring stations and comparison of column concentrations of nitrogen dioxide with satellite measurements.

The fully integrated 3-D, time dependant, hydrodynamic and sediment transport numerical model ECOMSED was used to simulate flow and sediment transport in rivers. ECOMSED was originally developed for large water bodies such as lakes and oceans and solves the primitive equations of RANS along with a second order turbulence model in an orthogonal curvilinear σ- coordinate system. The availability of the model as an open FORTRAN source code made modifications and addition of new models possible. A new bed load transport model was implemented in the code as well as improvements in treatment of river roughness parameterization, bed form effects, and automatic update of flow depth due to bed evolution. The model was applied to 1- km long reach of the River Klarälven, Sweden, where it bifurcates into two west and east channels. The water surface and the flow division in the channels were made in agreement with field data by spatially varying the roughness. However, the spatial distribution of the bed shear stress was not realistic. Improvements were made in the bottom boundary condition to represent the variable effects of bed forms on roughness depending on the flow regime and the flow depth. The improved model realistically reproduced the flow field as well as the sediment transport processes in the river Klarälven. / QC 20101123

The thesis proposes an optimization algorithm used a genetic algorithm. Optimization problem is demonstrated on issues of left turn which meets the requirements for systematic optimization - complexity of the problem, which can not be solved analytically, and the possibility of using templates, which enable automation of design and calculation of all variants.

Transport models are key in predicting travel behaviour and planning transport systems. Transport models can be either aggregated or disaggregated. Disaggregation means that travel behaviour is represented on an individual level, which can be beneficial because it offers a higher detail level and reduces aggregation bias. Input data for transport models can be both expensive and inaccessible, especially comprehensive data. Thus, it is advantageous to explore the utilisation of open data, which is free and accessible. The objective of the thesis was to evaluate how OpenStreetMap and other Open Data can be utilised in disaggregated transport modelling. The scope of the study was Uppsala, Sweden. In the thesis, a disaggregate transport model was designed, which only considered commuting trips made by public transport. Destinations and a synthetic population were estimated based on OpenStreetMap map features, SCB census data, and LuTRANS land use data. A travel survey was utilised in model calibration, and UL boarding data was used for model validation. The results showed that OpenStreetMap provided sufficient data for estimating a synthetic population and destinations for a disaggregate transport model when combined with other open data sources. Population and land usecensus data were essential for calibrating the model. However, the model came with limitations caused by assumptions, generalisation, technical constraints, and the partial incompleteness of open data. The thesis concludes that Open Data, such as OpenStreetMap, can be utilised sufficiently for transport modelling, with proper assumptions and processing. The openness of the data also increases the replicability of such a model. / Transportmodeller är viktiga i att förutspå resvanemönster och för att kunna planera transportsystemet. Transportmodeller kan vara antingen aggregeradeeller disaggregerade. Disaggregering betyder att resvanor är representerade påindividuell nivå, vilket kan vara fördelaktigt då det innebär en högre detalj nivå och mindre partiskhet orsakad av aggregering (aggregation bias). Indata förtransportmodeller kan vara både dyrt och svåråtkomligt, speciellt för mer omfattande data. Därav kan det vara till stor nytta att utforska möjligheten att använda öppnadata (Open Data), som är gratis och lättåtkomligt. Syftet med examensarbetetvar att utvärdera hur OpenStreetMap och annan Open Data kan användas idisaggregerad transportmodellering. Den geografiska omfattningen av studien är Uppsala tätort. En disaggregerad transportmodell togs fram i examensarbetet, sombara tog hänsyn till jobbresor med kollektivtrafik. Destinationer och en syntetiskbefolkning uppskattades utifrån OpenStreetMap objekt, befolkningsdata från SCB, samt markanvändningsdata från LuTRANS. En resvaneundersökning utnyttjadesför modellkalibrering och påstigningsdata från UL användes för modellvalidering.Resultaten visade att OpenStreetMap erbjöd tillräckligt med data för att ta framoch uppskatta en syntetisk befolkning och destinationer för en disaggregeradtransportmodell, om den kombineras med andra öppna datakällor. Befolkning- ochmarkanvändningsdata var avgörande i att kalibrera modellen. Dock så innefattar modellen vissa begränsningar som är orsakada av antaganden, generalisering, tekniskabegränsningar, samt ofullständigheten av Open Data. Slutsatsen är att Open Data, så som OpenStreetMap, kan utnyttjas för transportmodellering, om det kombineras med välformulerade antaganden och processering av datan. Datans öppenheten medför även en ökad replikerbarhet för en sådan modell.