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Stream channels and their corresponding riparian zones are composed of complex spatially and temporally dynamic systems. Changing land-use associated with urbanization has resulted in large shifts in riparian assemblages, stream hydraulics, and sediment dynamics leading to the degradation of the world’s waterways. To combat degradation, restoration and management of riparian zones is becoming increasingly common. However, the relationship between flora, especially the influence of invasive species, on sediment dynamics is poorly understood. This relationship must be studied further to ensure the success of management practices. Three methods were used to monitor erosion and turbidity within the Tookany Creek and its tributary Mill Run in the greater Philadelphia, PA region. To evaluate the influence of the invasive species Reynoutria japonica (Japanese knotweed) on erosion, reaches were chosen based on their riparian vegetation and degree of incision. Methods used to estimate sediment erosion included measuring changes in bank pins, repeated total station transects, and monitoring turbidity responses to storm events. While each method has been used in previous studies to monitor sediment flux, the combination of methods in this study allowed their applicability to be compared. Measurements taken with YSI turbidity loggers showed large fluctuations in turbidity based both on riparian conditions and geomorphic positioning, suggesting that future studies need to be careful with logger placement when using sediment calibration curves to estimate sediment yield within streams. There were pros and cons of using both total station and bank pins to estimate bank erosion. Total station has the potential to produce highly accurate measurements but a greater risk of loss of data if the control points used to establish the grid cannot be re-established from one measurement to the next. Bank pins are more likely to influence bank erosion and be affected by freeze-thaw conditions but provide a simple method of monitoring erosion at frequent intervals. Volume calculations based on total station transects along the main stem of the Tookany did not show a consistent relationship between riparian type and erosion rates. However, erosion calculations based on bank pins suggest greater erosion in reaches dominated by knotweed with 4.7x10-1 m3/m and 8.3x10-2 m3/m more erosion than those dominated by trees at Chelten Hills and Mill Run respectively. Turbidity responses to storm events were also higher (76.7 v 54.2 NTU) in reaches with knotweed, although this increase was found when the reach dominated by knotweed was also incised. Thus, this study linked knotweed to increased erosion using multiple methods. / Geology

According to the US Environmental Protection Agency (USEPA) sediment is a leading cause of water quality impairment (US EPA, 2002). The annual costs of sediment pollution in North America alone are estimated to range between $20 and $50 billion (Pimentel et al., 1995; Osterkamp et al, 1998, 2004). Due to the large spatial and temporal variations inherent in sediment transport, suspended sediment measurement is challenging. The overall goal of this research was to develop and test an inexpensive sensor for continuous suspended sediment monitoring in streams. This study was designed to determine if the gain and phase components of permittivity could be used to predict suspended sediment concentrations (SSC). A bench-scale suspension system was designed and tested to guarantee that there were no significant differences in the sediment suspension vertically or horizontally within the system. This study developed prediction models for SSC with input variables of temperature, specific conductivity, and gain and/or phase at multiple frequencies. The permittivity sensor is comprised of an electrode, power source, and a control box or frequency generator. Fixed and mixed effect, multiple, linear regression models were created and compared for target frequencies. However, it was not possible to meet the normality requirements for prediction accuracy. Partial Least Squares (PLS) regression techniques were also applied to gain and phase data for 127 of the 635 frequencies. The three models with the lowest error between predicted and actual values of SSC for validation were further tested with nine levels of independent validation data. The largest model error (error>50%) occurred for the top three models at 0 and 500 mg/L. At the higher concentrations error varied from 1-40%. Once the treatment levels, of the independent validation data set, were near 1000 mg/L the prediction accuracy increased for the top three models. Model 3A, a phase based model, preformed the best. Model 3A was able to predict six of the nine independent validation treatment levels within 300 mg/L. Future research will provide additional laboratory and field testing of the prototype sensor. / Ph. D.

Competing paradigms of community assembly emphasize different mechanisms for predicting patterns in biogeography. Niche assembly emphasizes the role of environmental gradients as filters that organize a metacommunity by locally selecting colonizers with similar functional traits, whereas dispersal assembly emphasizes the importance of source pool characteristics and dispersal limitation in organizing a metacommunity. In this study, I developed a framework that uses spatially explicit patterns in taxonomic and functional measures of community composition as diagnostics for community assembly processes for benthic macroinvertebrates in headwater streams in the southern Appalachians. Distance decay in taxonomic and functional similarity was used to determine the scales at which taxonomic turnover occurred within functional niches. Trait-neutral models of community composition were used as null models to assess which functional traits were the best candidates to explain how community composition was influenced by environmental gradients: an assessment of niche-based community assembly. Regional scale patterns suggested that niche-based community assembly was the dominant mechanism organizing community composition in headwater streams at local scales (<30km). Therefore, I compared how well trait-neutral models identified functional traits as relevant to community sorting against how well observed trait distributions correlated with environmental variation at a local scale, in the Ray Branch catchment (<10km study extent). Functional traits exhibiting non-random distributions within the Ray Branch watershed were most strongly correlated with environmental variation. Lastly, I assessed how the influences of niche and dispersal assembly on benthic macroinvertebrate community composition were affected by disturbance (shelterwood logging). Environmental variables defining the habitat template, and macroinvertebrate community composition, were measured before and after the disturbance; and path analysis was used to quantify the disturbance effect. The relationship between environmental variation and functional composition increased following logging, indicating disturbance augmented the influence of environmental filters, and consequently, the importance of niche-based community assembly. My dissertation provides the framework for a novel assessment of taxonomic and functional community composition data to infer the types of ecological dynamics that organize communities in the landscape. Additionally, this work provides a theoretical basis for assessing how dominant ecological processes change, in predictable ways, in response to changes in the habitat template. / Ph. D.

Concern about water quality has become an important environmental issue in the world, the United States, and Virginia. Volunteers have increasingly stepped forward to assist in the water quality monitoring task, and both state and federal protection agencies increasingly depend upon such voluntary assistance. The Izaak Walton League's Save Our Streams (SOS) is one such volunteer citizen water quality monitoring program. Recruiting, training, organizing and retaining volunteers are among the most resource intensive tasks of volunteer organizations. The purpose of this thesis is to document the motivations of SOS volunteers and the primary causes of their attrition in order to improve the SOS program as well as to enhance the experience of SOS volunteers. We also compared motivations of SOS volunteers, differences in SOS volunteers' evaluation of the program, and suggestions for improvements by varying participation levels in volunteerism. We found that SOS volunteers are primarily motivated by a desire to protect streams and to improve water quality. Learning about streams and teaching these concepts to others were also important motivations. Volunteers cited not enough time and having too many other obligations as the main reasons why they stopped participating in SOS activities. Recruitment and retention of SOS volunteers may be aided by providing feedback about how volunteer data are being used by protection agencies to protect streams, and providing opportunities for learning about streams and teaching these concepts to others. Lastly, we found that those volunteers who were most active in SOS differed in their motivations for participating, tended to be the most critical of the services and materials, and were most adamant about their data being used to protect streams. / Master of Science

In the United States, karst ecosystems cover approximately 20 percent of the country and karst aquifers provide 40 percent of the water used for drinking. In karst-influenced watersheds, karst features such as sinkholes and sinking streams act as rapid pathways for carrying water and pollutants into streams and groundwater. Human activities on karst landscapes can present some special problems such as alterations to hydrologic regime, contamination of groundwater, ground subsidence, and damage to cave ecosystems. Modeling a karst-influenced watershed can provide a better understanding of the interactions between surface and ground water and how water quality is affected by human activities. Several models were evaluated to determine their ability to model both discharge and nutrient transport in karst watersheds. The Soil Water Assessment Tool (SWAT) model was found to be appropriate due to its capability to represent almost all of the hydrological processes, its user-friendliness, and its ability to generate most of the parameters from available data. Moreover, SWAT can represent nitrogen transformations and transport processes and calculate nitrogen loadings, which is critical for karst watersheds. While it has been widely used and found to be an appropriate prediction tool, it does not explicitly include the capacity to represent specific features characteristic of karst-influenced basins. Baffaut and Benson (2008) modified the SWAT 2005 code to simulate faster aquifer recharge in karst environments, and this version was further modified here in the SWAT-Karst to represent karst environments at the HRU scale. A new parameter sink allows simulating the hydrology and nitrate transport in a sinkhole representing its unique landuse and soil characteristics, and a new parameter ss partitions nitrate transported with water that is lost from sinking streams. The SWAT-KARST model was used to simulate discharge and nitrogen loadings within the Opequon Creek karst-influenced watershed, located in the Potomac and Shenandoah River basin in Virginia and West Virginia. In the Opequon Creek watershed, SWAT-karst using the HRU to represent sinkholes had a more notable impact in the watershed hydrology than SWAT-B&B using a pond to represent sinkholes. Results of statistical evaluation show that SWAT-karst and the Baffaut and Benson (2008) version performed better than SWAT in predicting streamflow in a karst-influenced watershed. Although SWAT-karst showed almost the same performance as SWAT-B&B, SWAT-karst model offers the flexibility to represent the unique relationship between surface and ground water in karst features in an HRU. Using an HRU to represent sinkholes can depict the associated variability of a karst landscape. The new variables sink and ss provide a mechanism to represent the nutrient transport through sinkholes and sinking streams. Sensitivity analysis showed that SWAT-karst was sensitive to the new parameter sink which can be used for model calibration and to represent water recharge and nutrient transport to aquifers outside the watershed boundary. / Master of Science

Urban designers are trained to think systematically, to simultaneously see the big picture for numerous human systems in the city—including multiple modes of transportation, barriers faced by the city's inhabitants, and food and waste systems—and synthesize them into a coherent design. However, many urban designers use architecture as their sole means of shaping our cities, rather than employing other design disciplines as well. One solution to this limited focus on the built environment is "landscape urbanism." First appearing in the 1990s, landscape urbanism is a theory that argues that the best way to organize a city is through the design of its landscape, rather than the design of its buildings. At its best, landscape urbanism encourages a new way to understand cities: through the horizontal domain that acts as every city's connective tissue. At its worst, landscape urbanism can emphasize a purely aesthetic view of nature in the city, rather than recognizing its full potential as an additional functional system within the urban landscape. This failing of landscape urbanism can be addressed by its next evolution: ecological urbanism. As MIT Professor and Landscape Architect Anne Whiston Spirn writes in The Granite Garden, we need to recognize nature as "an essential force that permeates the city." By embracing the presence of nature's processes within the city, we can create an ecological urbanism that combines human and natural systems for the betterment of both. "The realization that nature is ubiquitous, a whole that embraces the city, has powerful implications for how the city is built and maintained and for the health, safety, and welfare of every resident" (Spirn). Currently, the Anacostia River and the neighborhoods to the east are neglected parts of Washington D.C., and most of the river's tributaries are buried underground. This neglect is similar to cities' historic disregard for the productive processes of nature, settling instead for a superficial, idealized abstraction of nature in the city. What if the city decided that instead of viewing urban streams as a nuisance that needed to be hidden, the Anacostia River and its tributary system could provide a beautiful, functional, and memorable organizational structure for the East of the River neighborhoods? Highlighting the presence of this large natural system within the city could be an opportunity to develop an "urban ecology" and frame our future relationship with nature. Using Washington DC's Anacostia River, its tributaries, and the East of the River neighborhoods as its framework, this thesis explores a possible step past landscape urbanism by advocating for an ecological urbanism that demonstrates how human and natural systems can work together in an urban environment in a way that is ecologically productive, regionally connected, and mutually beneficial. / Master of Science / Urban designers are trained to think systematically. They must simultaneously see the big picture for numerous human systems in the city—including multiple modes of transportation, barriers faced by the city’s inhabitants, and food and waste systems—and synthesize them into a coherent design. However, many urban designers use architecture as their sole means of shaping our cities, rather than employing other design disciplines as well. One solution to this limited focus on the built environment is “landscape urbanism” which recognizes that cities (like landscapes) are constantly undergoing processes of change. First appearing in the 1990s, landscape urbanism is a theory that argues that the best way to organize a city is through the design of its landscape, rather than the design of its buildings. At its best, landscape urbanism encourages a new way to understand cities: through the horizontal domain that acts as every city’s connective tissue. At its worst, landscape urbanism can emphasize a purely aesthetic view of nature in the city, rather than recognizing nature’s full potential as an additional functional system within the urban landscape. This failing of landscape urbanism can be addressed by its next evolution: ecological urbanism. As MIT Professor and Landscape Architect Anne Whiston Spirn writes in The Granite Garden, we need to recognize nature as “an essential force that permeates the city.” By embracing the presence of nature’s processes within the city, we can create an ecological urbanism that combines human and natural systems for the betterment of both. “The realization that nature is ubiquitous, a whole that embraces the city, has powerful implications for how the city is built and maintained and for the health, safety, and welfare of every resident” (Spirn 5). Currently, the Anacostia River and the neighborhoods to the east are neglected parts of Washington DC, and most of the river’s tributaries are buried underground. This neglect is similar to cities’ historic disregard for the productive processes of nature, settling instead for a superficial, idealized abstraction of nature in the city. What if the city decided that instead of viewing urban streams as a nuisance that needed to be hidden, the Anacostia River and its tributary system could provide a beautiful, functional, and memorable organizational structure for the East of the River neighborhoods? Highlighting the presence of this large natural system within the city could be an opportunity to develop an “urban ecology” and frame our future relationship with nature. Using Washington DC’s Anacostia River, its tributaries, and the East of the River neighborhoods as its framework, this thesis explores a possible step past landscape urbanism by advocating for an ecological urbanism that demonstrates how human and natural systems can work together in an urban environment in a way that is ecologically productive, regionally connected, and mutually beneficial.

Fluxos de dados são usualmente caracterizados por grandes quantidades de dados gerados continuamente em processos síncronos ou assíncronos potencialmente infinitos, em aplicações como: sistemas meteorológicos, processos industriais, tráfego de veículos, transações financeiras, redes de sensores, entre outras. Além disso, o comportamento dos dados tende a sofrer alterações significativas ao longo do tempo, definindo data streams evolutivos. Estas alterações podem significar eventos temporários (como anomalias ou eventos extremos) ou mudanças relevantes no processo de geração da stream (que resultam em alterações na distribuição dos dados). Além disso, esses conjuntos de dados podem possuir características espaciais, como a localização geográfica de sensores, que podem ser úteis no processo de análise. A detecção dessas variações de comportamento que considere os aspectos da evolução temporal, assim como as características espaciais dos dados, é relevante em alguns tipos de aplicação, como o monitoramento de eventos climáticos extremos em pesquisas na área de Agrometeorologia. Nesse contexto, esse projeto de mestrado propõe uma técnica para auxiliar a análise espaço-temporal em data streams multidimensionais que contenham informações espaciais e não espaciais. A abordagem adotada é baseada em conceitos da Teoria de Fractais, utilizados para análise de comportamento temporal, assim como técnicas para manipulação de data streams e estruturas de dados hierárquicas, visando permitir uma análise que leve em consideração os aspectos espaciais e não espaciais simultaneamente. A técnica desenvolvida foi aplicada a dados agrometeorológicos, visando identificar comportamentos distintos considerando diferentes sub-regiões definidas pelas características espaciais dos dados. Portanto, os resultados deste trabalho incluem contribuições para a área de mineração de dados e de apoio a pesquisas em Agrometeorologia. / Data streams are usually characterized by large amounts of data generated continuously in synchronous or asynchronous potentially infinite processes, in applications such as: meteorological systems, industrial processes, vehicle traffic, financial transactions, sensor networks, among others. In addition, the behavior of the data tends to change significantly over time, defining evolutionary data streams. These changes may mean temporary events (such as anomalies or extreme events) or relevant changes in the process of generating the stream (that result in changes in the distribution of the data). Furthermore, these data sets can have spatial characteristics such as geographic location of sensors, which can be useful in the analysis process. The detection of these behavioral changes considering aspects of evolution, as well as the spatial characteristics of the data, is relevant for some types of applications, such as monitoring of extreme weather events in Agrometeorology researches. In this context, this project proposes a technique to help spatio-temporal analysis in multidimensional data streams containing spatial and non-spatial information. The adopted approach is based on concepts of the Fractal Theory, used for temporal behavior analysis, as well as techniques for data streams handling also hierarchical data structures, allowing analysis tasks that take into account the spatial and non-spatial aspects simultaneously. The developed technique has been applied to agro-meteorological data to identify different behaviors considering different sub-regions defined by the spatial characteristics of the data. Therefore, results from this work include contribution to data mining area and support research in Agrometeorology.

Fluxos de dados são usualmente caracterizados por grandes quantidades de dados gerados continuamente em processos síncronos ou assíncronos potencialmente infinitos, em aplicações como: sistemas meteorológicos, processos industriais, tráfego de veículos, transações financeiras, redes de sensores, entre outras. Além disso, o comportamento dos dados tende a sofrer alterações significativas ao longo do tempo, definindo data streams evolutivos. Estas alterações podem significar eventos temporários (como anomalias ou eventos extremos) ou mudanças relevantes no processo de geração da stream (que resultam em alterações na distribuição dos dados). Além disso, esses conjuntos de dados podem possuir características espaciais, como a localização geográfica de sensores, que podem ser úteis no processo de análise. A detecção dessas variações de comportamento que considere os aspectos da evolução temporal, assim como as características espaciais dos dados, é relevante em alguns tipos de aplicação, como o monitoramento de eventos climáticos extremos em pesquisas na área de Agrometeorologia. Nesse contexto, esse projeto de mestrado propõe uma técnica para auxiliar a análise espaço-temporal em data streams multidimensionais que contenham informações espaciais e não espaciais. A abordagem adotada é baseada em conceitos da Teoria de Fractais, utilizados para análise de comportamento temporal, assim como técnicas para manipulação de data streams e estruturas de dados hierárquicas, visando permitir uma análise que leve em consideração os aspectos espaciais e não espaciais simultaneamente. A técnica desenvolvida foi aplicada a dados agrometeorológicos, visando identificar comportamentos distintos considerando diferentes sub-regiões definidas pelas características espaciais dos dados. Portanto, os resultados deste trabalho incluem contribuições para a área de mineração de dados e de apoio a pesquisas em Agrometeorologia. / Data streams are usually characterized by large amounts of data generated continuously in synchronous or asynchronous potentially infinite processes, in applications such as: meteorological systems, industrial processes, vehicle traffic, financial transactions, sensor networks, among others. In addition, the behavior of the data tends to change significantly over time, defining evolutionary data streams. These changes may mean temporary events (such as anomalies or extreme events) or relevant changes in the process of generating the stream (that result in changes in the distribution of the data). Furthermore, these data sets can have spatial characteristics such as geographic location of sensors, which can be useful in the analysis process. The detection of these behavioral changes considering aspects of evolution, as well as the spatial characteristics of the data, is relevant for some types of applications, such as monitoring of extreme weather events in Agrometeorology researches. In this context, this project proposes a technique to help spatio-temporal analysis in multidimensional data streams containing spatial and non-spatial information. The adopted approach is based on concepts of the Fractal Theory, used for temporal behavior analysis, as well as techniques for data streams handling also hierarchical data structures, allowing analysis tasks that take into account the spatial and non-spatial aspects simultaneously. The developed technique has been applied to agro-meteorological data to identify different behaviors considering different sub-regions defined by the spatial characteristics of the data. Therefore, results from this work include contribution to data mining area and support research in Agrometeorology.

No / It is challenging to use traditional data mining techniques to deal with real-time data stream classifications. Existing mining classifiers need to be updated frequently to adapt to the changes in data streams. To address this issue, in this paper we propose an adaptive ensemble approach for classification and novel class detection in concept drifting data streams. The proposed approach uses traditional mining classifiers and updates the ensemble model automatically so that it represents the most recent concepts in data streams. For novel class detection we consider the idea that data points belonging to the same class should be closer to each other and should be far apart from the data points belonging to other classes. If a data point is well separated from the existing data clusters, it is identified as a novel class instance. We tested the performance of this proposed stream classification model against that of existing mining algorithms using real benchmark datasets from UCI (University of California, Irvine) machine learning repository. The experimental results prove that our approach shows great flexibility and robustness in novel class detection in concept drifting and outperforms traditional classification models in challenging real-life data stream applications. (C) 2013 Elsevier Ltd. All rights reserved.