Environmental concerns are becoming increasingly relevant during a period of hemorrhaging ecosystem goods and services. Restoring these would result in positive outcomes for public health and economic benefit. This thesis seeks to address two environmental concerns: (1) accurate soil mapping and (2) treatment of nitrogen to affect water quality change.The current method of soil mapping, SSURGO (USDA‐NRCS Soil survey), is often erroneous and misleading. Two studies in this dissertation are conducted to evaluate the potential that different resolution digital elevation models (DEMs) have to distribute soil characteristics successfully. These studies are conducted in southwest Virginia and western Vermont. The aforementioned studies evaluated 36 and 59 soil samples, respectively. Spatial characteristics, including slope, catchment area, and topographic wetness, are derived from several DEMs. In chapter 2, these characteristics are spatially compared, and we found that small resolution rasters result in narrow flow paths relative to coarser rasters. In chapter 3, we isolate the analysis to focus on resolution size, instead of a mix of both resolution size and generation method. This is done by recursively coarsening small rasters, deriving spatial attributes from said rasters and evaluating their potential to fit the soil characteristics of interest. Here we found that slopes generated from resolutions smaller than 11m were poor predictors of soil characteristics. Both chapters are finished by proposing and evaluating a soil map. Proposed regressions beat SSURGO in all investigated properties. Furthermore, proposed maps consistently beat out uninformed smallest resolution derived maps.Chesapeake bay water quality managers are struggling to achieve targets for nitrogen loading. This is in part due to the widespread presence of legacy nitrogen. Legacy nitrogen is an emerging issue, and springs exporting high levels of nitrogen are not uncommon in northern Virginia. This thesis explores, in part, a novel concept of treating large loads of nitrogen exported from a spring with a bioreactor. Bioreactors are a young science that most typically pair carbon heavy subterranean receptacles to agricultural drainage. This provides a location for nitrogen fixing bacteria to consume nitrate/nitrite, turning these into inert nitrogen gas. A spring fed bioreactor is studied for 10 months, and bioreactor conditions including influent and effluent nitrogen concentrations, bioreactor flow, and temperature are collected. A model driven by first order reaction equations is found to be most accurate with inputs of temperature and bioreactor age. The resulting marginal effects of these inputs were consistent with previously reported studies. / Doctor of Philosophy / Centuries of industrialization have resulted in widespread human progress but have, at times, adversely impacted the environment. Constituents rely heavily on environmental services, such as clean air and water, to subsist. Environmental degradation has resulted in detrimental effects to public health, and remediation is currently economically viable. As such, there are strong incentives for researchers to understand environmental processes at a fundamental level. One such process is soil characteristic distribution. The distribution of soil characteristics, such as soil texture or organic matter, is especially important for agriculturalists, hydrologists and geotechnicians. Soil texture and organic matter distribution can affect crop yield, nitrogen export to surface waters, and structural stability of soils. Thus, accurate characterization of measured soil properties is paramount to multiple fields. The most typically used soil map is USDA‐NRCS Soil survey (commonly referred to as SSURGO). Currently, the SSURGO database is a poor predictor of soil characteristics. There is an opportunity to improve soil characteristic distribution using digital elevation models (DEMs). As DEMs become cheaper to develop, they are typically available in multiple resolutions and generation methods. In this research, several DEMs are used to better soil maps for watersheds in Southwest Virginia and Western Vermont. Both studies showed that DEMs can better distribute soils when compared to the current SSURGO maps. Additionally, we showed that the finest resolution dataset was not always best, and mixed resolution topographic wetness indices to be most advantageous for distributing soils.Another such process is remediation of surface waters from high loads of nitrogen and phosphorus. The Haber-Bosch method of producing nitrogen fertilizer is one of the most important human innovations in recent history. This method is likely responsible for the aversion of widespread famine in the early 1900s. However, residents of multiple river systems, including the Chesapeake Bay and the Mississippi River, are suffering from the adverse effects of widespread hypoxic/anoxic (with little/no oxygen, respectively) zones within water. These have partially been responsible for the decline of commercial ventures such as fisheries and tourism. These zones are caused by eutrophication, a process of unsustainable plant growth in the presence of nitrogen and phosphorus. Water quality managers typically target agricultural runoff and point source polluters when trying to eliminate anthropogenic nitrogen. However, legacy nitrogen (nitrogen stored in groundwater in excess of a year) has become an emerging concern for water quality. It is not uncommon for springs in karst areas to be contaminated with high concentrations of nitrogen. These springs present a point source that can be treated by an emerging technology: bioreactors. Bioreactors are subterranean, woodchip filled basins that provide a location for microbes to exchange water soluble nitrogen for inert nitrogen gas. The consistency in nitrogen loading and constant flow provide stability relative to more traditional bioreactor installations. Most typically, bioreactors are installed downstream of agricultural drainage systems, and influent flow and nitrogen load depend wholly on precipitation/irrigation and nitrogen application. In this thesis, a novel spring fed bioreactor is studied. Removal rates of nitrogen are quantified using a regression driven by reaction kinetics. The analysis showed bioreactor efficiency was intimately related to hydraulic residence time, nitrogen loading, bioreactor bed temperature, and bioreactor age. The spring fed bioreactor is found to be advantageous because of its consistency, and disadvantages because springs are colder and thus less efficient than typical irrigated runoff.

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The simple accumulation-based network identification method (ANIM) in a raster Geographic Information System (GIS) posed by O'Callaghan and Mark (1984) has been criticized for producing a spatially uniform drainage density (Tarboton 2002) at the watershed scale. This criticism casts doubt on the use of ANIMs for deriving properties such as overland flow length for nonpoint source pollution models, without calibrating the accumulation threshold value. However, the basic assumption that underlies ANIMs is that convergent topography will yield a more rapid accumulation of cells, and thus, more extensive flow networks, with divergent, or planar terrain yielding sparser networks. Previous studies have focused on networks that are coarser than the hill-slope scale, and have relied upon visual inspection of drainage networks to suggest that ANIMs lack the ability to produce diverse networks. In this study overland flow lengths were calculated on a sub-watershed basis, with standard deviation, and range calculated for sub-watershed populations as a means of quantifying the diversity of overland flow lengths produced by ANIM at the hill slope scale. Linear regression and Spearman ranking analyses were used to determine if the methods represented trends in overland flow length as suggested by manual delineation of contour lines. Three ANIMs were analyzed: the flow accumulation method (O'Callaghan and Mark, 1984), the terrain curvature method (Tarboton, 2000) and the ridge accumulation method (introduced in this study). All three methods were shown to produce non-zero standard deviations and ranges using a single support area threshold, with the terrain curvature method producing the most diverse networks, followed by the ridge accumulation method, and then the flow accumulation method. At an analysis unit size of 20 ha, the terrain curvature method produced a standard deviation that was most similar to those suggested by the contour crenulations, -13.5%, followed by the ridge accumulation method, -21.5%, and the flow accumulation method, -61.6%. The ridge accumulation produced the most similar range, -19.1%, followed by terrain curvature, -24.9%, and flow accumulation, -65.4%. While the flow accumulation networks had a much narrower range of predicted flow lengths, it had the highest Spearman ranking coefficient, Rs=0.722, and linear regression coefficient, R2=0.602. The terrain curvature method was second, Rs=0.641, R2=0.469, and then ridge accumulation, Rs=0.602, R2=0.490. For all methods, as threshold values were varied, areas of dissimilar morphology (as evidenced by the common stream metric stream frequency) experienced changes in overland flow lengths at different rates. This results in an inconsistency in ranking of sub-watersheds at different thresholds. When thresholds were varied to produce average overland flow lengths from 75 m to 150 m, the terrain curvature method showed the lowest incidence of rank change, 16.05%, followed by the ridge accumulation method, 16.73%, then flow accumulation, 25.18%. The results of this investigation suggest that for all three methods, a causal relationship exists between threshold area, underlying morphology, and predicted overland flow length. This causal relationship enables ANIMs to represent contour network trends in overland flow length with a single threshold value, but also results in the introduction of rank change error as threshold values are varied. Calibration of threshold value (varying threshold in order to better match observed overland flow lengths) is an effective means of increasing the accuracy of ANIM predictions, and may be necessary when comparing areas with different stream frequencies. It was shown that the flow accumulation method produces less diverse networks than the terrain curvature and ridge accumulation methods. However, the results of rank and regression analyses suggest that further investigation is required to determine if these more diverse ANIM are in fact more accurate than the flow accumulation method. / Master of Science

Soil is one of the most precious resources on Earth because of its role in storing and recycling water and nutrients essential for life, providing a variety of ecosystem services. This vulnerable resource is at risk from degradation by erosion, salinity, contamination and other effects of mismanagement. Information from soil is therefore crucial for its sustainable management. While the demand for soil information is growing, the quantity of data collected in the field is reducing due to financial constraints. Digital Soil Mapping (DSM) supports the creation of geographically referenced soil databases generated by using field observations or legacy data coupled, through quantitative relationships, with environmental covariates. This enables the creation of soil maps at unexplored locations at reduced costs. The selection of an optimal scale for environmental covariates is still an unsolved issue affecting the accuracy of DSM. The overall aim of this research was to explore the effect of spatial scale alterations of environmental covariates in DSM. Three main targets were identified: assessing the impact of spatial scale alterations on classifying soil taxonomic units; investigating existing approaches from related scientific fields for the detection of scale patterns and finally enabling practitioners to find a suitable scale for environmental covariates by developing a new methodology for spatial scale analysis in DSM. Three study areas, covered by detailed reconnaissance soil survey, were identified in the Republic of Ireland. Their different pedological and geomorphological characteristics allowed to test scale behaviours across the spectrum of conditions present in the Irish landscape. The investigation started by examining the effects of scale alteration of the finest resolution environmental covariate, the Digital Elevation Model (DEM), on the classification of soil taxonomic units. Empirical approaches from related scientific fields were subsequently selected from the literature, applied to the study areas and compared with the experimental methodology. Wavelet analysis was also employed to decompose the DEMs into a series of independent components at varying scales and then used in DSM analysis of soil taxonomic units. Finally, a new multiscale methodology was developed and evaluated against the previously presented experimental results. The results obtained by the experimental methodology have proved the significant role of scale alterations in the classification accuracy of soil taxonomic units, challenging the common practice of using the finest available resolution of DEM in DSM analysis. The set of eight empirical approaches selected in the literature have been proved to have a detrimental effect on the selection of an optimal DEM scale for DSM applications. Wavelet analysis was shown effective in removing DEM sources of variation, increasing DSM model performance by spatially decomposing the DEM. Finally, my main contribution to knowledge has been developing a new multiscale methodology for DSM applications by combining a DEM segmentation technique performed by k-means clustering of local variograms parameters calculated in a moving window with an experimental methodology altering DEM scales. The newly developed multiscale methodology offers a way to significantly improve classification accuracy of soil taxonomic units in DSM. In conclusion, this research has shown that spatial scale analysis of environmental covariates significantly enhances the practice of DSM, improving overall classification accuracy of soil taxonomic units. The newly developed multiscale methodology can be successfully integrated in current DSM analysis of soil taxonomic units performed with data mining techniques, so advancing the practice of soil mapping. The future of DSM, as it successfully progresses from the early pioneering years into an established discipline, will have to include scale and in particular multiscale investigations in its methodology. DSM will have to move from a methodology of spatial data with scale to a spatial scale methodology. It is now time to consider scale as a key soil and modelling attribute in DSM.

The main objective of the diploma thesis is creation of methodology and automatization of calibration process of network graph based on the values of morphometric characteristics and motion vectors. The resulting morphometric values of the terrain have been detected on the basis of mathematical and cartographic methods for line course expression. The most accurate one has been used in GIS network analysis over the road network and digital terrain models, which were chosen as the most appropriate for this purpose. Relationship between morphometric values and values suitable for use in network analysis (speed, time, ...) has been studied using specific examples in appropriately selected territory with using selected vehicle, which was designated as a bicycle. The practical part for the verification of functionality of the suggested methodology has been compared with other models of accessibility, available web-map portals and route planners. The process of transport network evaluation based on selected parameters has been automated in Python programming language as a tool in ArcGIS software, which is attached to the diploma thesis. Powered by TCPDF (www.tcpdf.org)

The city of Bukavu, located at the eastern border of the Democratic Republic of Congo in the province of South Kivu, is a large and densely populated urban agglomeration that has experienced rapid growth during recent years. At the same time, Bukavu has been repeatedly struck by environmental hazards, especially by devastating landslides. The steepness of slopes in the city’s hilly and mountainous terrain is one of the most important factors contributing to landslide susceptibility, but the anthropogenic impact resulting from uncoordinated urban sprawl and land cover change additionally plays a crucial role in exacerbating the vulnerability of neighbourhoods. This thesis utilizes GIS software to provide cartographic material for landslide risk assessment in Bukavu and the city’s surroundings. It examines risk exposure related to slope inclination of densely built-up areas, the spatial development of the city and urban growth tendencies, and complements these aspects with information about land cover and the terrain.

O uso de geotecnologias pode contribuir de forma muito significativa para os estudos em geomorfologia. Considerando os principais componentes desta ciência, morfografia, morfometria, morfogênese e morfocronologia, os modelos digitais para a representação da superfície da Terra podem ser amplamente utilizados na aquisição de muitas destas informações. O uso de Modelos Digitais de Elevação (MDE) há alguns anos, já é uma realidade em estudos envolvendo geomorfologia. A sua utilização permite a aquisição de variáveis e parâmetros objetivos que podem servir à definição de critérios para o agrupamento de unidades geomorfológicas. Podendo, portanto, ser um instrumento valioso para mapeamento de áreas amplas em escalas de 1:50.000 e 1:100.000. Para tanto, é necessário definir os critérios coerentes e os algoritmos de segmentação que oferecem os melhores resultados para as diversas áreas de estudo. Os MDE gerados pela missão Shuttle Radar Topography Mission (SRTM) são de vasta abrangência e contemplam todo o território nacional brasileiro. Portanto, os dados gerados pela missão podem ser uma importante fonte de informação para mapeamentos com metodologia única. O alcance deste objetivo não garante avanços metodológicos na cartografia geomorfológica, tendo em vista que a possibilidade de comparação entre diferentes cartas geomorfológicas de detalhe ainda é restrita. / Geotechnologies can contribute significantly to geomorphology studies. Whereas the main principles of this science, mophography, morphometry, morphogenesis and morphochronology, the digital models used to represent the Earth surface can be widely utilized in a bunch of these data. Lately, the use of Digital Elevation Model (DEM) can be considered a reality in geomorphology studies. The utilization allows the acquisition of objective variables and parameters that can be suitable for definition of geomorphological units. Hence, can be a valuable tool for wide area mapping using 1:50.000 and 1:100.000 scales. For that reason, it is necessary to define coherent criteria and the proper segmentation algorithm in order to reach better results for different study cases. DEM provided by Shuttle Radar Topography Mission (SRTM) are wide range and cover the whole national territory. Therefore, data provided by this mission can be an important information for a single methodology mapping project.

The aim of this thesis was to create potential distribution models for Dicranum majus (Greater Fork Moss) and Polytrichum alpinum (Alpine Haircap) in Bohemian Switzerland National Park. In the Czech Republic these bryophyte species occur in cold climatic regions typically with higher altitudes. In Bohemian and Saxon Switzerland they can occur in really low altitudes thanks to unique microclimatic conditions of deep inversion ravines. These bryophyte species had low number of occurence records in studied area before the start of my research (4 occurence localities for Dicranum majus, 8 occurence localities for Polytrichum alpinum). Predictive habitat suitability models can be an effective tool for selecting potential new occurence localities, planning field research or management design. During field research I recorded 34 new occurence localities for Dicranum majus and 29 new occurence localities for Polytrichum alpinum in Bohemian Switzerland National Park. I used 8 topographic parameters derived from digital elevation model with 1 m resolution as environmental data. Using these data I created models of potential distribution of the most suitable habitats for both species with algorithms Artificial neural networks (ANN), Generalised linear model (GLM) and Random forest (RF). RF algorithm had the...

The spatial patterns of land surface and subsurface characteristics determine the spatial heterogeneity of hydrological processes. Soil depth is one of these characteristics and an important input parameter required by distributed hydrological models that explicitly represent spatial heterogeneity. Soil is related to topography and land cover due to the role played by topography and vegetation in affecting soil-forming processes. The research described in this dissertation addressed the development of statistical models that predict the soil depth pattern over the landscape; derivation of new topographic variables evaluated using both serial and parallel algorithms; and evaluation of the impacts of detailed soil depth representation on simulations of stream flow and soil moisture. The dissertation is comprised of three papers. In paper 1, statistical models were developed to predict soil depth pattern over the watershed based on topographic and land cover variables. Soil depth was surveyed at locations selected to represent the topographic and land cover variation at the Dry Creek Experimental Watershed, near Boise, Idaho. Explanatory variables were derived from a digital elevation model and remote sensing imagery for regression to the field data. Generalized Additive and Random Forests models were developed to predict soil depth over the watershed. The models were able to explain about 50% of the soil depth spatial variation, which is an important improvement over the soil depth extracted from the SSURGO national soil database. In paper 2, definitions of the new topographic variables derived in the effort to model soil depth, and serial and Message Passing Interface parallel implementations of the algorithms for their evaluation are presented. The parallel algorithms enhanced the processing speed of large digital elevation models as compared to the serial recursive algorithms initially developed. In paper 3, the impact of spatially explicit soil depth information on simulations of stream flow and soil moisture as compared to soil depth derived from the SSURGO soil database has been evaluated. The Distributed Hydrology Vegetation Soil Model was applied using automated parameter optimization technique with all input parameters the same except soil depth. Stream flow was less impacted by the detailed soil depth information, while simulation of soil moisture was slightly improved due to the detailed representation of soil depth.