Methane fluxes were calculated from measurements carried out at a temperate upland forest in Central Ontario using the eddy covariance method over five months in the summer and fall seasons of 2011. Measurements were made by an off-axis integrated cavity output spectrometer Fast Greenhouse Gas Analyzer (FGGA) which simultaneously measured methane (CH4), carbon dioxide (CO2), and water at 10 Hz sampling rates. Observed methane fluxes showed net uptake of methane over the measurement period with an average uptake flux value (±standard deviation of the mean) of -2.7±0.13 nmol m-2 s-1. Methane fluxes showed a diurnal pattern of increased uptake during the day and increasing uptake with seasonal progression. There was also a significant correlation in methane fluxes with soil water content and wind speed. Comparison of the FGGA measurements to those using a static chamber method and canister sampling showed close agreement in flux and mixing ratio values respectively.

Methane fluxes were calculated from measurements carried out at a temperate upland forest in Central Ontario using the eddy covariance method over five months in the summer and fall seasons of 2011. Measurements were made by an off-axis integrated cavity output spectrometer Fast Greenhouse Gas Analyzer (FGGA) which simultaneously measured methane (CH4), carbon dioxide (CO2), and water at 10 Hz sampling rates. Observed methane fluxes showed net uptake of methane over the measurement period with an average uptake flux value (±standard deviation of the mean) of -2.7±0.13 nmol m-2 s-1. Methane fluxes showed a diurnal pattern of increased uptake during the day and increasing uptake with seasonal progression. There was also a significant correlation in methane fluxes with soil water content and wind speed. Comparison of the FGGA measurements to those using a static chamber method and canister sampling showed close agreement in flux and mixing ratio values respectively.

Genetic data analysis has been capturing a lot of attentions for understanding the mechanism of the development and progressing of diseases like cancers, and is crucial in discovering genetic markers and treatment targets in medical research. This dissertation focuses on several important issues in genetic data analysis, graphical network modeling, feature selection, and covariance estimation. First, we develop a gene network modeling method for discrete gene expression data, produced by technologies such as serial analysis of gene expression and RNA sequencing experiment, which generate counts of mRNA transcripts in cell samples. We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution. We derive the gene network structures by selecting covariance matrices of the Gaussian distribution with a hyper-inverse Wishart prior. We incorporate prior network models based on Gene Ontology information, which avails existing biological information on the genes of interest. Next, we consider a variable selection problem, where the variables have natural grouping structures, with application to analysis of chromosomal copy number data. The chromosomal copy number data are produced by molecular inversion probes experiments which measure probe-specific copy number changes. We propose a novel Bayesian variable selection method, the hierarchical structured variable se- lection (HSVS) method, which accounts for the natural gene and probe-within-gene architecture to identify important genes and probes associated with clinically relevant outcomes. We propose the HSVS model for grouped variable selection, where simultaneous selection of both groups and within-group variables is of interest. The HSVS model utilizes a discrete mixture prior distribution for group selection and group-specific Bayesian lasso hierarchies for variable selection within groups. We further provide methods for accounting for serial correlations within groups that incorporate Bayesian fused lasso methods for within-group selection. Finally, we propose a Bayesian method of estimating high-dimensional covariance matrices that can be decomposed into a low rank and sparse component. This covariance structure has a wide range of applications including factor analytical model and random effects model. We model the covariance matrices with the decomposition structure by representing the covariance model in the form of a factor analytic model where the number of latent factors is unknown. We introduce binary indicators for estimating the rank of the low rank component combined with a Bayesian graphical lasso method for estimating the sparse component. We further extend our method to a graphical factor analytic model where the graphical model of the residuals is of interest. We achieve sparse estimation of the inverse covariance of the residuals in the graphical factor model by employing a hyper-inverse Wishart prior method for a decomposable graph and a Bayesian graphical lasso method for an unrestricted graph.

Large-scale mining practices have disturbed many natural watersheds in northern Alberta, Canada. To restore disturbed landscapes and ecosystems functions, reconstruction strategies have been adopted with the aim of establishing sustainable reclaimed lands. The success of the reconstruction process depends on the design of reconstruction strategies, which can be optimized by improving the understanding of the controlling hydrological processes in the reconstructed watersheds. Evapotranspiration is one of the important components of the hydrological cycle; its estimation and analysis are crucial for better assessment of the reconstructed landscape hydrology, and for more efficient design. The complexity of the evapotranspiration process and its variability in time and space has imposed some limitations on previously developed evapotranspiration estimation models. The vast majority of the available models estimate the rate of potential evapotranspiration, which occurs under unlimited water supply condition. However, the rate of actual evapotranspiration (AET) depends on the available soil moisture, which makes its physical modeling more complicated than the potential evapotranspiration. The main objective of this study is to estimate and analyze the AET process in a reconstructed landscape.<p> Data driven techniques can model the process without having a complete understanding of its physics. In this study, three data driven models; genetic programming (GP), artificial neural networks (ANNs), and multilinear regression (MLR), were developed and compared for estimating the hourly eddy covariance (EC)-measured AET using meteorological variables. The AET was modeled as a function of five meteorological variables: net radiation (Rn), ground temperature (Tg), air temperature (Ta), relative humidity (RH), and wind speed (Ws) in a reconstructed landscape located in northern Alberta, Canada. Several ANN models were evaluated using two training algorithms of Levenberg-Marquardt and Bayesian regularization. The GP technique was employed to generate mathematical equations correlating AET to the five meteorological variables. Furthermore, the available data were statistically analyzed to obtain MLR models and to identify the meteorological variables that have significant effect on the evapotranspiration process. The utility of the investigated data driven models was also compared with that of HYDRUS-1D model, which is a physically based model that makes use of conventional Penman-Monteith (PM) method for the prediction of AET. HYDRUS-1D model was examined for estimating AET using meteorological variables, leaf area index, and soil moisture information. Furthermore, Wavelet analysis (WA), as a multiresolution signal processing tool, was examined to improve the understanding of the available time series temporal variations, through identifying the significant cyclic features, and to explore the possible correlation between AET and the meteorological signals. WA was used with the purpose of input determination of AET models, a priori.<p> The results of this study indicated that all three proposed data driven models were able to approximate the AET reasonably well; however, GP and MLR models had better generalization ability than the ANN model. GP models demonstrated that the complex process of hourly AET can be efficiently modeled as simple semi-linear functions of few meteorological variables. The results of HYDRUS-1D model exhibited that a physically based model, such as HYDRUS-1D, might perform on par or even inferior to the data driven models in terms of the overall prediction accuracy. The developed equation-based models; GP and MLR, revealed the larger contribution of net radiation and ground temperature, compared to other variables, to the estimation of AET. It was also found that the interaction effects of meteorological variables are important for the AET modeling. The results of wavelet analysis demonstrated the presence of both small-scale (2 to 8 hours) and larger-scale (e.g. diurnal) cyclic features in most of the investigated time series. Larger-scale cyclic features were found to be the dominant source of temporal variations in the AET and most of the meteorological variables. The results of cross wavelet analysis indicated that the cause and effect relationship between AET and the meteorological variables might vary based on the time-scale of variation under consideration. At small time-scales, significant linear correlations were observed between AET and Rn, RH, and Ws time series, while at larger time-scales significant linear correlations were observed between AET and Rn, RH, Tg, and Ta time series.

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Les fonctions de sensibilité dites a posteriori permettent de caractériser des corrections aux conditions initiales qui peuvent réduire significativement l'erreur de prévision à une échéance donnée (typiquement 24 ou 48 heures). L'erreur est ici définie par l'écart à une analyse de vérification et la fonction de sensibilité ne peut donc être calculée qu'a posteriori. De telles structures dépendent de la nature de l'écoulement et ne sont pas prises en compte dans le modèle de covariance d'erreur de prévision stationnaire utilisé dans un système d'assimilation de données variationnelle 3D (3D-Var) comme celui du Centre Météorologique Canadien (CMC). Pour remédier à ceci, Hello et Bouttier (2001) ont introduit une formulation différente des covariances d'erreur de prévision qui permet d'inclure les fonctions de structure basées sur des fonctions de sensibilité a priori définissant la structure de changements aux conditions initiales qui ont le plus d'impact sur une prévision d'échéance donnée. Dans ce cas, l'amplitude de cette correction est déterminée en s'ajustant aux observations disponibles. Dans ce projet, une formulation différente est proposée et comparée à celle de Hello et Bouttier (2001). L'algorithme, appelé 3D-Var adapté, est tout d'abord présenté et analysé dans le cadre plus simple d'une analyse variationnelle 1D (1D-Var) pour être ensuite introduit dans le 3D-Var du CMC. L'impact du changement apporté a été étudié en utilisant les fonctions de sensibilité a posteriori associées à une prévision manquée sur la côte est de l'Amérique du Nord. En mesurant globalement l'erreur de prévision, la fonction de sensibilité indique qu'il est nécessaire d'apporter des corrections à l'analyse sur différentes régions du globe. Pour le 3D-Var adapté, ceci conduit à une fonction de structure non localisée et l'amplitude de la correction est caractérisée par un seul paramètre défini par l'ensemble des observations disponibles. En comparant aux prévisions issues du 3D-Var opérationnel ou de l'analyse de sensibilité, la prévision issue de l'analyse du 3D-Var adapté est améliorée par rapport à celle du 3D-Var conventionel mais nettement moins que celle issue de l'analyse de sensibilité. Par contre, le 3D-Var adapté améliore l'ajustement de l'analyse aux observations alors que l'analyse de sensibilité le dégrade. En localisant la mesure de l'erreur de prévision sur la région correspondant au système météorologique du cas étudié sur la côte est de l'Amérique du Nord, la fonction de sensibilité est maintenant localisée sur une région mieux délimitée (dite région sensible). Il est également possible de varier la fenêtre temporelle utilisée pour définir la fonction de sensibilité. L'impact sur la qualité de l'analyse et des prévisions résultantes a été étudié autant pour l'analyse de sensibilité que pour le 3D-Var adapté. Les résultats montrent que la définition d'une fonction de structure appropriée pour un système d'assimilation vise à simultanément concorder aux observations disponibles et améliorer la qualité des prévisions. Les résultats obtenus montrent que l'utilisation des fonctions de sensibilité comme fonctions de structures n'est pas immédiate. Bien que limitées à un seul cas, nos expériences indiquent certaines pistes intéressantes pour définir des fonctions de sensibilité pouvant être utilisées comme fonctions de structures. Ces idées pourraient s'appliquer également aux fonctions de sensibilité a priori.

The boreal forest is one of the worlds largest forest biomes and comprises a major portion of the terrestrial carbon (C) sink. Quantifying the net C change in forest ecosystems is an important step in understanding and modeling the global C cycle. The goals of this project were: to estimate and compare the total change in ecosystem C over a 10-year period in two boreal forest stands using biometric and eddy-covariance approaches, and to evaluate the year-to-year changes in C uptake. This study utilized 10 years of eddy-covariance data and ecosys model data from the Old Aspen (OA) and Old Jack Pine (OJP) sites in central Saskatchewan, part of the Boreal Ecosystem Research and Monitoring Sites (BERMS). According to the eddy-covariance and C stock approaches, between 1994 and 2004 the net change in C storage at OA was 15.6 ± 4.0 and 18.2 ± 8.0 Mg C ha-1, respectively. At OJP, the 10-year net change in C storage from eddy-covariance was 5.8 ± 2.0 Mg C ha-1 in comparison to 6.9 ± 1.6 Mg C ha-1 from the carbon stock approach. While both sites were sinks of C between 1994 and 2004, the greatest increase in C occurred in different components - the forest floor at OA (14.6 Mg C ha-1) and in the living vegetation at OJP (8.0 Mg C ha-1). In 2004, total ecosystem C content was greater at OA (180.6 Mg C ha-1) than OJP (78.9 Mg C ha-1), with 50% (OA) and 39% (OJP) of the C in the detritus and mineral soil pools. During the 10-year period of eddy-covariance measurements, there was a positive correlation between both annual and growing season gross ecosystem photosynthesis (GEP) and live stem C biomass increment at OA, whereas no significant relationships were found at OJP. Stem C increment accounted for 30% of total net primary productivity (NPP) at both sites, and NPP/GEP ratios were 0.36 and 0.32 at OA and OJP, respectively. Overall, this study found good agreement between eddy-covariance and biometric estimates of ecosystem C change at OA and OJP between 1994 and 2004. Over that period at OA, eddy-covariance estimates of photosynthesis captured the inter-annual variability in C uptake based on the growth of tree rings.

Large-scale mining practices have disturbed many natural watersheds in northern Alberta, Canada. To restore disturbed landscapes and ecosystems functions, reconstruction strategies have been adopted with the aim of establishing sustainable reclaimed lands. The success of the reconstruction process depends on the design of reconstruction strategies, which can be optimized by improving the understanding of the controlling hydrological processes in the reconstructed watersheds. Evapotranspiration is one of the important components of the hydrological cycle; its estimation and analysis are crucial for better assessment of the reconstructed landscape hydrology, and for more efficient design. The complexity of the evapotranspiration process and its variability in time and space has imposed some limitations on previously developed evapotranspiration estimation models. The vast majority of the available models estimate the rate of potential evapotranspiration, which occurs under unlimited water supply condition. However, the rate of actual evapotranspiration (AET) depends on the available soil moisture, which makes its physical modeling more complicated than the potential evapotranspiration. The main objective of this study is to estimate and analyze the AET process in a reconstructed landscape.<p> Data driven techniques can model the process without having a complete understanding of its physics. In this study, three data driven models; genetic programming (GP), artificial neural networks (ANNs), and multilinear regression (MLR), were developed and compared for estimating the hourly eddy covariance (EC)-measured AET using meteorological variables. The AET was modeled as a function of five meteorological variables: net radiation (Rn), ground temperature (Tg), air temperature (Ta), relative humidity (RH), and wind speed (Ws) in a reconstructed landscape located in northern Alberta, Canada. Several ANN models were evaluated using two training algorithms of Levenberg-Marquardt and Bayesian regularization. The GP technique was employed to generate mathematical equations correlating AET to the five meteorological variables. Furthermore, the available data were statistically analyzed to obtain MLR models and to identify the meteorological variables that have significant effect on the evapotranspiration process. The utility of the investigated data driven models was also compared with that of HYDRUS-1D model, which is a physically based model that makes use of conventional Penman-Monteith (PM) method for the prediction of AET. HYDRUS-1D model was examined for estimating AET using meteorological variables, leaf area index, and soil moisture information. Furthermore, Wavelet analysis (WA), as a multiresolution signal processing tool, was examined to improve the understanding of the available time series temporal variations, through identifying the significant cyclic features, and to explore the possible correlation between AET and the meteorological signals. WA was used with the purpose of input determination of AET models, a priori.<p> The results of this study indicated that all three proposed data driven models were able to approximate the AET reasonably well; however, GP and MLR models had better generalization ability than the ANN model. GP models demonstrated that the complex process of hourly AET can be efficiently modeled as simple semi-linear functions of few meteorological variables. The results of HYDRUS-1D model exhibited that a physically based model, such as HYDRUS-1D, might perform on par or even inferior to the data driven models in terms of the overall prediction accuracy. The developed equation-based models; GP and MLR, revealed the larger contribution of net radiation and ground temperature, compared to other variables, to the estimation of AET. It was also found that the interaction effects of meteorological variables are important for the AET modeling. The results of wavelet analysis demonstrated the presence of both small-scale (2 to 8 hours) and larger-scale (e.g. diurnal) cyclic features in most of the investigated time series. Larger-scale cyclic features were found to be the dominant source of temporal variations in the AET and most of the meteorological variables. The results of cross wavelet analysis indicated that the cause and effect relationship between AET and the meteorological variables might vary based on the time-scale of variation under consideration. At small time-scales, significant linear correlations were observed between AET and Rn, RH, and Ws time series, while at larger time-scales significant linear correlations were observed between AET and Rn, RH, Tg, and Ta time series.

<p>A large portion of the anthropogenic emissions of greenhouse gases (<italic>GHG</italic>s) are cycled through the terrestrial biosphere. Quantifying the exchange of these gases between the terrestrial biosphere and the atmosphere is critical to constraining their atmospheric budgets now and in the future. These fluxes are governed by biophysical processes like photosynthesis, transpiration, and microbial respiratory processes which are driven by factors like meteorology, disturbance regimes, and long term climate and land cover change. These complex processes occur over a broad range of temporal (seconds to decades) and spatial (millimeters to kilometers) scales, necessitating the application of simplifying models to forecast fluxes at the scales required by climate mitigation and adaptation policymakers. </p><p>Over the long history of biophysical research, much progress has been made towards developing appropriate models for the biosphere-atmosphere exchange of <italic>GHG</italic>s. Many processes are well represented in model frameworks, particularly at the leaf scale. However, some processes remain poorly understood, and models do not perform robustly over coarse spatial scales and long time frames. Indeed, model uncertainty is a major contributor to difficulties in constraining the atmospheric budgets of greenhouse gases. </p><p>The central objective of this dissertation is to reduce uncertainty in the quantification and forecasting of the biosphere-atmosphere exchange of greenhouse gases by addressing a diverse array of research questions through a combination of five unique field experiments and modeling exercises. In this first chapter, nocturnal evapotranspiration -- a physiological process which had been largely ignored until recent years -- is quantified and modeled in three unique ecosystems co-located in central North Carolina, U.S.A. In the second chapter, more long-term drivers of evapotranspiration are explored by developing and testing theoretical relationships between plant water use and hydraulic architecture that may be readily incorporated into terrestrial ecosystem models. The third chapter builds on this work by linking key parameters of carbon assimilation models to structural and climatic indices that are well-specified over much of the land surface in an effort to improve model parameterization schemes. The fourth chapter directly addresses questions about the interaction between physiological carbon cycling and disturbance regimes in current and future climates, which are generally poorly represented in terrestrial ecosystem models. And the last chapter explores effluxes of methane and nitrous oxide (which are historically understudied) in addition to CO₂ exchange in a large temperate wetland ecosystem (which is an historically understudied biome). While these five case studies are somewhat distinct investigations, they all: a) are all grounded in the principles of biophysics, b) rely on similar measurement and mathematical modeling techniques, and c) are conducted under the governing objective of reducing measurement and model uncertainty in the biosphere-atmosphere exchange of greenhouse gases.</p> / Dissertation