Land surface processes play an important role when modeling weather and climate, and understanding and representing such processes in South America is a particular challenge because of the large variations in regional climate and surface features such as vegetation and soil. Numerical models have been used to explore the climate and weather of continental South America, but without appropriate initiation of land surface conditions model simulations can rapidly diverge from reality. This initiation problem is exacerbated by the fact that conventional surface observations over South America are scarce and biased towards the urban centers and coastal areas. This dissertation explores issues related to the apt representation of land surface processes and their impacts in numerical simulations with a regional atmospheric model (specifically the Eta model) over South America. The impacts of vegetation heterogeneity in regional weather forecast were first investigated. A South American Land Data Assimilation System (SALDAS) was then created analogous to that currently used in North America to estimate soil moisture fields for initializing regional atmospheric models. The land surface model (LSM) used in this SALDAS is the Simplified Simple Biosphere (SSiB). Precipitation fields are critical when calculating soil moisture and, because conventional surface observations are scarce in South America, some of the most important remote sensed precipitation products were evaluated as potential precipitation forcing for the SALDAS. Spin up states for SSiB where then compared with climatological estimates of land surface fields and significant differences found. Finally, an assessment was made of the value of SALDAS-derived soil moisture fields on Eta model forecasts. The primary result was that model performance is enhanced over the entire continent in up to 72h forecasts using SALDAS surface fields

No description available.

The current study examined the interrelationships among womanist identity, cultural identity, acculturation, and self-esteem in 74 Asian American women who are currently enrolled in or who have graduated from a college or university in the United States. It was hypothesized that Internalization attitudes, cultural identity, and acculturation would predict self-esteem among Asian American women. It was also hypothesized that cultural identity (Ethnic Identification) and acculturation would be negatively correlated with each other. Results of the simultaneous multiple regression analysis indicated that Internalization attitudes and cultural identity were both significant predictors of self-esteem. Asian American women with higher levels of Internalization attitudes had higher levels of self-esteem, consistent with Ossana, Helms, and Leonard's (1992) study. Asian American women with higher levels of Marginal attitudes had lower levels of self-esteem. Results, however, yielded no significant relationship between acculturation and self-esteem. A correlational analysis revealed a significant negative correlation between cultural identity (Ethnic Identification) and acculturation, confirming Lee's (1988) assertion that acculturation decreases cultural identity. / Department of Counseling Psychology and Guidance Services

In the year 2000, croplands covered about 12% of the Earth’s ice-free land surface. Through cropland management, humankind momentarily appropriates about 25% of terrestrial ecosystem productivity. Not only are croplands a key element of human food supply, but also bear potential in increased carbon (C) uptake when best-practice land management approaches are adopted. A detailed assessment of the impact of land use on terrestrial ecosystems can be achieved by modelling, but the simulation of crop C cycling itself is a relatively new discipline. Observational data on crop net ecosystem exchange (NEE) are available only recently, and constitute an important tool for model development, diagnosis, and validation. Before crop functional types (CFT) had been introduced, however, large-scale biogeochemical models (BGCM) lacked crop-specific patterns of phenology, C allocation, and land management. As a consequence, the influence of cropland C cycling on biosphere-atmosphere C exchange seasonality and magnitude is currently poorly known. To date, no regional assessment of crop C cycling and yield formation exists that specifically accounts for spatially and temporally varying patterns of sowing dates within models. In this thesis, I present such an assessment for the first time. In the first step (chapter 2), I built a crop C mass balance model (SPAc) that models crop development and C allocation as a response to ambient meteorological conditions. I compared model outputs against C flux and stock observations of six different sites in Europe, and found a high degree of agreement between simulated and measured fluxes (R2 = 0.83). However, the model tended to overestimate leaf area index (LAI), and underestimate final yield. In a model comparison study (chapter 3), I found in cooperation with further researchers that SPAc best reproduces observed fluxes of C and water (owed to the model’s high temporal and process resolution), but is deficient due to a lack in simulating full crop rotations. I then conducted a detailed diagnosis of SPAc through the assimilation of C fluxes and biometry with the Ensemble Kalman Filter (EnKF, chapter 4), and identified potential model weaknesses in C allocation fractions and plant hydraulics. Further, an overestimation of plant respiration and seasonal leaf thickness variability were evident. Temporal parameter variability as a response to C flux data assimilation (DA) is indicative of ecosystem processes that are resolved in NEE data but are not captured by a model’s structure. Through DA, I gained important insights into model shortcomings in a quantitative way, and highlighted further needs for model improvement and future field studies. Finally, I developed a framework allowing for spatio-temporally resolved simulation of cropland C fluxes under observational constraints on land management and canopy greenness (chapter 5). MODIS (Moderate Resolution Imaging Spectroradiometer) data were assimilated both variationally (for sowing date estimation) and sequentially (for improved model state estimation, using the EnKF) into SPAc. In doing so, I was able to accurately quantify the multiannual (2000-2006) regional C flux and biometry seasonality of maize-soybean crop rotations surrounding the Bondville Ameriflux eddy covariance (EC) site, averaged over 104 pixel locations within the wider area. Results show that MODIS-derived sowing dates and the assimilation of LAI data allow for highly accurate simulations of growing season C cycling at locations for which groundtruth sowing dates are not available. Through quantification of the spatial variability in biometry, NEE, and net biome productivity (NBP), I found that regional patterns of land management are important drivers of agricultural C cycling and major sources of uncertainty if not appropriately accounted for. Observing C cycling at one single field with its individual sowing pattern is not sufficient to constrain large-scale agroecosystem behaviour. Here, I developed a framework that enables modellers to accurately simulate current (i.e. last 10 years) C cycling of major agricultural regions and their contribution to atmospheric CO2 variability. Follow-up studies can provide crucial insights into testing and validating large-scale applications of biogeochemical models.

Skolan ska ge elever kunskaper om tidsbegreppet för att de ska verka i samhället. Tidsbegreppet är ett matematikområde som innehåller olika delar som elever i slutet av årskurs 6 ska kunna. Problemet är att elever visar svårigheter om tidsbegreppet redan i årskurs 3. Den här studien syftar till en undersökning om elever i årskurs 3:s kunskaper om tid. Den syftar även till lärarens användning av olika metoder, för att eleverna ska få en förståelse för tidsbegreppet. I studien observeras en matematiklektion om tidsbegreppet, diagnoser genomförs och intervjuer sker med en lärare samt tre elever. Resultatet visar att eleverna har god kunskap om den analoga klockan samt tidsenheter. Elevernas svårigheter är kunskapen om den digitala klockan samt tidsskillnader.

This thesis presents a study of changing constructions and perceptions of cultural identity over the period 300 BC to AD 100 in the region of northern central Spain known in antiquity as Celtiberia. Its primary focus is iconography, with images of male and female figures of particular interest. The iconography is used to map the continuities and discontinuities in a sense of Celtiberian identity, and considers the effect that interaction with non-Celtiberians, including Celts and Iberians but especially with Romans, had on this identity. A theoretical framework in which to study 'cultural identity' is proposed in the Prolegomena. After the Prolegomena, the thesis is divided into six chapters. Chapter One, Celtiberia in its Historical and Cultural Context, examines the development of Celtiberian culture and Celtiberian settlements over time, and the changes that occurred after the arrival of Romans. Chapter Two, Metallurgy and Metal Objects, looks at three categories of metal objects (fibulae, hospitium tesserae, and armaments) and asks whether the horseman motif, an important iconographic element in this thesis, is emblematic of a 'warrior aristocracy'. Chapter Three, Human and Animal Figures on Painted Pottery, studies the range of human figures found on Celtiberian ceramic vessels, considering the types of scenes and figures that were most popular. Chapter Four, Coins from Pre-Roman and Early Imperial Celtiberia, traces the development of numismatic images in the region. This chapter emphasises the so-called transitional coins, which represent the first time that Celtiberian cities were publicly identified with Roman authority on official media. Chapter Five, Men's Funerary Monuments, returns to critical analysis of the horseman motif, focusing on stelai with relief images of male figures on horseback. Chapter Six, Women's Funerary Monuments, examines the most popular visual language for Celtiberian women, the 'funerary banquet,' and places stelai bearing this theme in their wider social context. A concluding section discusses Celtiberian iconography as a whole. It also considers the role that language - Celtiberian and/or Latin - played alongside the images, and whether the phenomena of bilingualism and Latinisation of names bear 'cultural identity' significance.

As sensor data becomes more and more available, there is an increasing interest in assimilating real time sensor data into spatial temporal simulations to achieve more accurate simulation or prediction results. Particle Filters (PFs), also known as Sequential Monte Carlo methods, hold great promise in this area as they use Bayesian inference and stochastic sampling techniques to recursively estimate the states of dynamic systems from some given observations. However, PFs face major challenges to work effectively for complex spatial temporal simulations due to the high dimensional state space of the simulation models, which typically cover large areas and have a large number of spatially dependent state variables. As the state space dimension increases, the number of particles must increase exponentially in order to converge to the true system state. The purpose of this dissertation work is to develop localized particle filtering to support PFs-based data assimilation for large-scale spatial temporal simulations. We develop a spatially dependent particle-filtering framework that breaks the system state and observation data into sub-regions and then carries out localized particle filtering based on these spatial regions. The developed framework exploits the spatial locality property of system state and observation data, and employs the divide-and-conquer principle to reduce state dimension and data complexity. Within this framework, we propose a two-level automated spatial partitioning method to provide optimized and balanced spatial partitions with less boundary sensors. We also consider different types of data to effectively support data assimilation for spatial temporal simulations. These data include both hard data, which are measurements from physical devices, and soft data, which are information from messages, reports, and social network. The developed framework and methods are applied to large-scale wildfire spread simulations and achieved improved results. Furthermore, we compare the proposed framework to existing particle filtering based data assimilation frameworks and evaluate the performance for each of them.

Bacteria classified in the family Methylomonadaceae must derive their carbon from one-carbon compounds. They are characterized by the possession of internal membranes of two types. Type I membranes are layered and fill the middle of the cells while type II membranes form concentric layers around the periphery of the cells. Also, there are two metabolic pathways by which the methylobacteria assimilate one-carbon compounds. Further evidence of this dichotomy was sought by DNA-DNA saturation hybridization of DNAs from both types of methylobacteria. Very low DNA-DNA homology was seen between types I and II or within the types. It was not possible, therefore, to correlate the degree of genetic relatedness with either the nature of the internal membranes or the pathway of carbon assimilation.

Process-based models are used in a diverse array of fields, including environmental engineering to provide supporting information to engineers, policymakers and stakeholdes. Recent advances in remote sensing and data storage technology have provided opportunities for improving the application of process-based models and visualizing data, but also present new challenges. The availability of larger quantities of data may allow models to be constructed and calibrated in a more thorough and precise manner, but depending on the type and volume of data, it is not always clear how to incorporate the information content of these data into a coherent modeling framework. In this context, using process-based models in new ways to provide decision support or to produce more complete and flexible predictive tools is a key task in the modern data-rich engineering world. In standard usage, models can be used for simulating specific scenarios; they can also be used as part of an automated design optimization algorithm to provide decision support or in a data-assimilation framework to incorporate the information content of ongoing measurements. In that vein, this thesis presents and demonstrates extensions and refinements to leverage the best of what process-based models offer using Differential Evolution (DE) the Extended Kalman Filter (EKF). Coupling multi-objective optimization to a process-based model may provide valuable information provided an objective function is constructed appropriately to reflect the multi-objective problem and constraints. That, in turn, requires weighting two or more competing objectives in the early stages of an analysis. The methodology proposed here relaxes that requirement by framing the model optimization as a sensitivity analysis. For demonstration, this is implemented using a surface water model (HEC-RAS) and the impact of floodplain access up and downstream of a fixed bridge on bridge scour is analyzed. DE, an evoutionary global optimization algorithm, is wrapped around a calibrated HEC-RAS model. Multiple objective functions, representing different relative weighting of two objectives, are used; the resulting rank-orders of river reach locations by floodplain access sensitivity are consistent across these multiple functions. To extend the applicability of data assimilation methods, this thesis proposes relaxing the requirement that the model be calibrated (provided the parameters are still within physically defensible ranges) before performing assimilation. The model is then dynamically calibrated to new state estimates, which depend on the behavior of the model. Feasibility is demonstrated using the EKF and a synthetic dataset of pendulum motion. The dynamic calibration method reduces the variance of prediction errors compared to measurement errors using an initially uncalibrated model and produces estimates of calibration parameters that converge to the true values. The potential application of the dynamic calibration method to river sediment transport modeling is proposed in detail, including a method for automated calibration using sediment grain size distribution as a calibration parameter.

No description available.