Carbon dynamics and management in Canadian boreal forests : triplex-flux model development, validation, and applications

La forêt boréale, seconde aire biotique terrestre sur Terre, est actuellement considérée comme un réservoir important de carbone pour l'atmosphère. Les modèles basés sur le processus des écosystèmes terrestres jouent un rôle important dans l'écologie terrestre et dans la gestion des ressources naturelles. Cette thèse examine le développement, la validation et l'application aux pratiques de gestion des forêts d'un tel modèle. Tout d'abord, le module récemment développé d'échange du carbone TRIPLEX-Flux (avec des intervalles de temps d'une demi heure) est utilisé pour simuler les échanges de carbone des écosystèmes d'une forêt au peuplement boréal et mixte de 75 ans dans le nord est de l'Ontario, d'une forêt avec un peuplement d'épinette noire de 110 ans localisée dans le sud de Saskatchewan, et d'une forêt avec un peuplement d'épinette noire de 160 ans située au nord du Manitoba au Canada. Les résultats des échanges nets de l'écosystème (ENE) simulés par TRIPLEX-Flux sur l'année 2004 sont comparés à ceux mesurés par les "tours de mesures de covariance des turbulences" et montrent une bonne correspondance générale entre les simulations du modèle et les observations de terrain. Le coefficient de détermination moyen (R2) est approximativement de 0.77 pour le peuplement mixte boréal, et de 0.62 et 0.65 pour les deux forêts d'épinette noire situées au centre du Canada. Le modèle est capable d'intégrer les variations diurnes de l'échange net de l'écosystème (ENE) de la période de pousse (de mai à août) de 2004 sur les trois sites. Le peuplement boréal mixte ainsi que les peuplements d'épinette noire agissaient tous deux comme des réservoirs de carbone pour l'atmosphère durant la période de pousse de 2004. Cependant le peuplement boréal mixte montre une plus grande productivité de l'écosystème, un plus grand piégeage du carbone ainsi qu'un meilleur taux de carbone utilisé comparé aux peuplements d'épinette noire. L'analyse de la sensibilité a mis en évidence une différence de sensibilité entre le matin et le milieu de journée, ainsi qu'entre une concentration habituelle et une concentration doublée de CO2. De plus, la comparaison de différents algorithmes pour calculer la conductance stomatale a montré que la production nette de l'écosystème (PNE) modélisée, utilisant une itération d'algorithme est conforme avec les résultats utilisant des rapports Ci/Ca constants de 0.74 et de 0.81 respectivement pour les concentrations courantes et doublées de CO2. Une variation des paramètres et des données variables de plus ou moins 10% a entrainé, respectivement pour les concentrations courantes et doublées de CO2, une réponse du modèle inférieure ou égale à 27.6% et à 27.4%. La plupart des paramètres sont plus sensibles en milieu de journée que le matin excepté pour ceux en lien avec la température de l'air, ce qui suggère que la température a des effets considérables sur la sensibilité du modèle pour ces paramètres/variables. L'effet de la température de l'air était plus important dans une atmosphère dont la concentration de CO2 était doublée. En revanche, la sensibilité du modèle au CO2 qui diminuait lorsque la concentration de CO2 était doublée. \ud Sachant que, les incertitudes de prédiction des modèles proviennent majoritairement des hétérogénéités spatio-temporelles au cœur des écosystèmes terrestres, à la suite du développement du modèle et de l'analyse de sa sensibilité, sept sites forestiers à tour de mesures de flux (comportant trois forêts à feuilles caduques, trois forêts tempérées à feuillage persistant et une forêt boréale à feuillage persistant) ont été sélectionnés pour faciliter la compréhension des variations mensuelles des paramètres du modèle. La méthode de Monte Carlo par Markov Chain (MCMC) à été appliquée pour estimer les paramètres clefs de la sensibilité dans le modèle basé sur le processus de l'écosystème, TRIPLEX-Flux. Les quatre paramètres clefs sélectionnés comportent: un taux maximum de carboxylation photosynthétique à 25°C (Vmax), un taux du transport d'un électron (Jmax) saturé en lumière lors du cycle photosynthétique de réduction du carbone, un coefficient de conductance stomatale (m), et un taux de référence de respiration à 10°C (R10). Les mesures de covariance des flux turbulents du CO2 échangé ont été assimilées afin d'optimiser les paramètres pour tous les mois de l'année 2006. Après que l'optimisation et l'ajustement des paramètres ait été réalisée, la prédiction de la production nette de l'écosystème s'est améliorée significativement (d'environ 25%) en comparaison avec les mesures de flux de CO2 réalisés sur les sept sites d'écosystèmes forestiers. Les résultats suggèrent, dans le respect des paramètres sélectionnés, qu'une variabilité plus importante se produit dans les forêts à feuilles larges que dans les forêts d'arbres à aiguilles. De plus, les résultats montrent que l'approche par la fusion des données du modèle incorporant la méthode MCMC peut être utilisée pour estimer les paramètres basés sur les mesures de flux, et que des paramètres saisonniers optimisés peuvent considérablement améliorer la précision d'un modèle d'écosystème lors de la simulation de sa productivité nette et cela pour différents écosystèmes forestiers situés à travers l'Amérique du Nord. Finalement, quelques uns de ces paramètres et algorithmes testés ont été utilisés pour mettre à jour l'ancienne version de TRIPLEX comportant des intervalles de temps mensuels. En outre, le volume d'un peuplement et la quantité de carbone de la biomasse au dessus du sol des forêts d'épinette noire au Québec sont simulés en relation avec un peuplement des âges, cela à des fins de gestion forestière. Ce modèle a été validé en utilisant à la fois une tour de mesure de flux et des données d'un inventaire forestier. Les simulations se sont avérées réussies. Les corrélations entre les données observées et les données simulées (R2) étaient de 0.94, 0.93 et 0.71 respectivement pour le diamètre à l.3 m, la moyenne de la hauteur du peuplement et la productivité nette de l'écosystème. En se basant sur les résultats à long terme de la simulation, il est possible de déterminer l'âge de maturité du carbone du peuplement considéré comme prenant place à l'époque où le peuplement de la forêt prélève le maximum de carbone, avant que la récolte finale ne soit réalisée. Après avoir comparé l'âge de maturité du volume des peuplements considérés (d'environ 65 ans) et l'âge de maturité du carbone des peuplements considérés (d'environ 85 ans), les résultats suggèrent que la récolte d'un même peuplement à son âge de maturité de volume est prématuré. Décaler la récolte d'environ vingt ans et permettre au peuplement considéré d'atteindre l'âge auquel sa maturité du carbone prend place, mènera à la formation d'un réservoir potentiellement important de carbone. Aussi, un nouveau diagramme de la gestion de la densité du carbone du peuplement considéré, basé sur les résultats de la simulation, a été développé pour démontrer quantitativement les relations entre les densités de peuplement, le volume de peuplement et la quantité de carbone de la biomasse au dessus du sol à des stades de développement variés, dans le but d'établir des régimes de gestion de la densité optimaux pour le rendement de volume et le stockage du carbone. \ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : écosystème forestier, flux de CO2, production nette de l'écosystème, eddy covariance, TRIPLEX-Flux module, validation d'un modèle, Markov Chain Monte Carlo, estimation des paramètres, assimilation des données, maturité du carbone, diagramme de gestion de la densité de peuplemen

A novel feature selection approach based on tree models for evaluating the punching shear capacity of steel fiber-reinforced concrete flat slabs

When designing flat slabs made of steel fiber-reinforced concrete (SFRC), it is very important to predict their punching shear capacity accurately. The use of machine learning seems to be a great way to improve the accuracy of empirical equations currently used in this field. Accordingly, this study utilized tree predictive models (i.e., random forest (RF), random tree (RT), and classification and regression trees (CART)) as well as a novel feature selection (FS) technique to introduce a new model capable of estimating the punching shear capacity of the SFRC flat slabs. Furthermore, to automatically create the structure of the predictive models, the current study employed a sequential algorithm of the FS model. In order to perform the training stage for the proposed models, a dataset consisting of 140 samples with six influential components (i.e., the depth of the slab, the effective depth of the slab, the length of the column, the compressive strength of the concrete, the reinforcement ratio, and the fiber volume) were collected from the relevant literature. Afterward, the sequential FS models were trained and verified using the above-mentioned database. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices, including the coefficient of determination (R2) and root mean square error (RMSE), were utilized. The results obtained from the experiments indicated that the FS-RT model outperformed FS-RF and FS-CART models in terms of prediction accuracy. The range of R2 and RMSE values were obtained as 0.9476–0.9831 and 14.4965–24.9310, respectively; in this regard, the FS-RT hybrid technique demonstrated the best performance. It was concluded that the three hybrid techniques proposed in this paper, i.e., FS-RT, FS-RF, and FS-CART, could be applied to predicting SFRC flat slabs

On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils

The California Bearing Ratio (CBR) is an important index for evaluating the bearing capacity of pavement subgrade materials. In this research, random subspace optimization-based hybrid computing models were trained and developed for the prediction of the CBR of soil. Three models were developed, namely reduced error pruning trees (REPTs), random subsurface-based REPT (RSS-REPT), and RSS-based extra tree (RSS-ET). An experimental database was compiled from a total of 214 soil samples, which were classified according to AASHTO M 145, and included 26 samples of A-2-6 (clayey gravel and sand soil), 3 samples of A-4 (silty soil), 89 samples of A-6 (clayey soil), and 96 samples of A-7-6 (clayey soil). All CBR tests were performed in soaked conditions. The input parameters of the models included the particle size distribution, gravel content (G), coarse sand content (CS), fine sand content (FS), silt clay content (SC), organic content (O), liquid limit (LL), plastic limit (PL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). The accuracy of the developed models was assessed using numerous performance indexes, such as the coefficient of determination, relative error, MAE, and RMSE. The results show that the highest prediction accuracy was obtained using the RSS-based extra tree optimization technique

Prediction of iceberg-seabed interaction using machine learning algorithms

Every year thousands of icebergs are born out of glaciers in the Arctic zone and carried away by the currents and winds into the North Atlantic. These icebergs may touch the sea bottom in shallow waters and scratch the seabed, an incident called “ice-gouging”. Ice-gouging may endanger the integrity of the buried subsea pipelines and power cables because of subgouge soil displacement. In other words, the shear resistance of the soil causes the subgouge soil displacement to extend much deeper than the ice keel tip. This, in turn, may cause the displacement of the pipelines and cables buried deeper than the most possible gouge depth. Determining the best burial depth of the pipeline is a key design aspect and needs advanced continuum numerical modeling and costly centrifuge tests. Empirical equations suggested by design codes may be also used but they usually result in an over-conservative design. Iceberg management, i.e., iceberg towing and re-routing, is currently the most reliable approach to protect the subsea and offshore structures, where the approaching icebergs are hooked and towed in a safe direction. Iceberg management is costly and involves a range of marine fleets and advanced subsea survey tools to determine the iceberg draft, etc. The industry is constantly looking for cost-effective and quick alternatives to predict the iceberg draft and subgouge soil displacements. In this study, powerful machine learning (ML) algorithms were used as an alternative cost-effective approach to first screen the threatening icebergs by determining their drafts and then to predict the subgouge soil displacement to be fed into the structural integrity analysis. Developing a reliable solution to predict the iceberg draft and subgouge soil displacement requires a profound understanding of the problem's dominant parameters. Therefore, the present study started with dimensional analyses to identify the dimensionless groups of key parameters governing the physics of the problem. Two comprehensive datasets were constructed using the monitored characteristics of the real icebergs for draft prediction and experimental studies for the subgouge soil displacements reported in the literature. Using the constructed database, 14 ML algorithms ranging from neural network-based (NN-based) to three-based methods were sequentially used to predict the iceberg draft and the subgouge soil displacement. The studies were conducted both in clay and sand seabed. By different combinations of the input parameters, several ML models were developed and assessed by performing sensitivity analysis, error analysis, discrepancy analysis, uncertainty analysis, and partial derivative sensitivity analysis to identify the superior ML models along with the most influential input parameters. The best ML model was able to predict the iceberg drafts alongside the subgouge soil features with the highest level of precision, correlation, and lowest degree of complexity. A set of ML-based explicit equations were also derived from the wide range of field and experimental measurements for the estimation of iceberg drafts, subgouge soil deformations, and ice keel reaction forces, which outperformed the existing empirical equations. The study resulted in developing a set of tools that can be used for both a cost-effective screening of the threatening icebergs and the prediction of the corresponding subgouge soil displacements. The outcome of the study can effectively contribute to a significant reduction of iceberg management costs and greenhouse gas (GHG) emissions through the mitigation of the marine spread operation

Carbon and energy exchange of semi-arid ecosystems with heterogeneous canopy structure

Carbon and energy fluxes were measured with the eddy covariance technique above two semi-arid ecosystems, ponderosa pine and juniper/sagebrush, located in central Oregon. The two ecosystems have low LAI and a very open canopy structure. The energy closure was ~70-80% at both ecosystems, equivalent to an imbalance of 150-250 W m⁻² on cloudless summer days, when net radiation (R[subscript n]) was ~600-700 W m⁻². The lack of closure cannot be explained by the uncertainty of an estimate of available energy due to a single R[subscript n] sensor location. At the more open juniper/sagebrush ecosystem, a numerical model showed that spatial variation in R[subscript n], even for large differences in surface radiation temperature and reflection coefficient between ecosystem components (soil and vegetation), is less than 10% of measured R[subscript n]. The uncertainty in R[subscript n] at the two-layered ponderosa pine ecosystem with patches of young and old-growth trees is expected to be smaller than at the juniper ecosystem. Net carbon exchange (NEE) at the pine site strongly depends on environmental factors effecting carbon assimilation (A[subscript c]) and ecosystem respiration (R[subscript e]). A more detailed analysis of the carbon budget showed a strong negative response of carbon uptake to large vapor pressure deficits (VPD), whereas water vapor exchange (LE) was less affected. At large VPD the vegetation maintains a sustainable water flow through the soil-plant system by stomatal control of transpiration. The stomatal closure leads to limitation in A[subscript c], but LE is subject to a positive feedback from higher evaporative demand. Annual NEE of the ponderosa pine forest (200-300 gC m⁻²) was in the mid-range of reported NEE of temperate forest ecosystems, though, unusually, much of the annual carbon gain occurred during the fall through spring, because the relatively mild winters allowed carbon assimilation to occur and R[subscript e] rates were low. The information gathered at our ponderosa pine site during two years with contrasting climate suggests that the carbon uptake of the ponderosa pine ecosystem will be more sensitive to global climate change than the water vapor exchange

Agriculture in responsibility for our common world

The vocational training course program Agriculture in Responsibility for our common World organised within the frame of the Banat Green Deal Project GreenERDE (Education and Research in the context of the digital and ecological transformation of agriculture in the Banat Region and Baden-Württemberg - towards resource efficiency and resilience) and delivered between June 2021 and May 2022 targets the knowledge and experience transfer to the farmer community in the Banat Region, Romania and other parts of the world. Current and future challenges, such as the ecological conversion and digital transformation of agricultural production, but also social, economic and cultural aspects haven been addressed transcending prevailing patterns. The innovative and relevant knowledge originating from practice, experiments, research or development projects throughout Europe and other continents is presented in a training format for interested participants.Das im Rahmen des Banat Green Deal Projekts GreenERDE (Bildung und Forschung im Kontext der digitalen und ökologischen Transformation des Agrarbereichs im Banat und Baden-Württemberg - auf dem Weg zu Ressourceneffizienz und Resilienz) von Juni 2021 bis Mai 2022 durchgeführte Fortbildungsprogramm Landwirtschaft in Verantwortung für unsere gemeinsame Welt), zielt auf den Wissens- und Erfahrungstransfer unter Landwirten und anderen interessirten Personen in der Banat-Region, Rumänien und anderen Teilen der Welt ab. Aktuelle und zukünftige Herausforderungen, wie die ökologische Umstellung und die digitale Transformation der landwirtschaftlichen Produktion, aber auch soziale, wirtschaftliche und kulturelle Aspekte wurden jenseits vorherrschender Muster adressiert. Das innovative und relevante Wissen aus Praxis, Forschungs- oder Entwicklungsprojekten in ganz Europa und anderen Kontinenten wird in einem Trainingsformat für interessierte Teilnehmer präsentiert

Climate Change and Environmental Sustainability-Volume 4

Anthropogenic activities are significant drivers of climate change and environmental degradation. Such activities are particularly influential in the context of the land system that is an important medium connecting earth surface, atmospheric dynamics, ecological systems, and human activities. Assessment of land use land cover changes and associated environmental, economic, and social consequences is essential to provide references for enhancing climate resilience and improving environmental sustainability. On the one hand, this book touches on various environmental topics, including soil erosion, crop yield, bioclimatic variation, carbon emission, natural vegetation dynamics, ecosystem and biodiversity degradation, and habitat quality caused by both climate change and earth surface modifications. On the other hand, it explores a series of socioeconomic facts, such as education equity, population migration, economic growth, sustainable development, and urban structure transformation, along with urbanization. The results of this book are of significance in terms of revealing the impact of land use land cover changes and generating policy recommendations for land management. More broadly, this book is important for understanding the interrelationships among life on land, good health and wellbeing, quality education, climate actions, economic growth, sustainable cities and communities, and responsible consumption and production according to the United Nations Sustainable Development Goals. We expect the book to benefit decision makers, practitioners, and researchers in different fields, such as climate governance, crop science and agricultural engineering, forest ecosystem, land management, urban planning and design, urban governance, and institutional operation.Prof. Bao-Jie He acknowledges the Project NO. 2021CDJQY-004 supported by the Fundamental Research Funds for the Central Universities and the Project NO. 2022ZA01 supported by the State Key Laboratory of Subtropical Building Science, South China University of Technology, China. We appreciate the assistance of Mr. Lifeng Xiong, Mr. Wei Wang, Ms. Xueke Chen, and Ms. Anxian Chen at School of Architecture and Urban Planning, Chongqing University, China

Quantitative Approaches to the Genomics of Clonal Evolution

Many problems in the biological sciences reduce to questions of genetic evolution. Entire classes of medical pathology, such as malignant neoplasia or infectious disease, can be viewed in the light of Darwinian competition of genomes. With the benefit of today's maturing sequencing technologies we can observe and quantify genetic evolution with nucleotide resolution. This provides a molecular view of genetic material that has adapted, or is in the process of adapting, to its local selection pressures. A series of problems will be discussed in this thesis, all involving the mathematical modeling of genomic data derived from clonally evolving populations. We use a variety of computational approaches to characterize over-represented features in the data, with the underlying hypothesis that we may be detecting fitness-conferring features of the biology. In Part I we consider the cross-sectional sampling of human tumors via RNA-sequencing, and devise computational pipelines for detecting oncogenic gene fusions and oncovirus infections. Genomic translocation and oncovirus infection can each be a highly penetrant alteration in a tumor's evolutionary history, with famous examples of both populating the cancer biology literature. In order to exert a transforming influence over the host cell, gene fusions and viral genetic programs need to be expressed and thus can be detected via whole transcriptome sequencing of a malignant cell population. We describe our approaches to predicting oncogenic gene fusions (Chapter 2) and quantifying host-viral interactions (Chapter 3) in large panels of human tumor tissue. The alterations that we characterize prompt the larger question of how the genetics of tumors and viruses might vary in time, leading us to the study of serially sampled populations. In Part II we consider longitudinal sampling of a clonally evolving population. Phylogenetic trees are the standard representation of a clonal process, an evolutionary picture as old as Darwin's voyages on the Beagle. Chapter 4 first reviews phylogenetic inference and then introduces a certain phylogenetic tree space that forms the starting point of our work on the topic. Specifically, Chapter 4 describes the construction of our projective tree space along with an explicit implementation for visualizing point clouds of rescaled trees. The Chapter finishes by defining a method for stable dimensionality reduction of large phylogenies, which is useful for analyzing long genomic time series. In Chapter 5 we consider medically relevant instances of clonal evolution and the longitudinal genetic data sets to which they give rise. We analyze data from (i) the sequencing of cancers along their therapeutic course, (ii) the passaging of a xenografted tumor through a mouse model, and (iii) the seasonal surveillance of H3N2 influenza's hemagglutinin segment. A novel approach to predicting influenza vaccine effectiveness is demonstrated using statistics of point clouds in tree spaces. Our investigations into clonal processes may be extended beyond naturally occurring genomes. In Part III we focus on the directed clonal evolution of populations of synthetic RNAs in vitro. Analogous to the selection pressures exerted upon malignant cells or viral particles, these synthetic RNA genomes can be evolved against a desired fitness objective. We investigate fitness objectives related to reprogramming ribosomal translation. Chapter 6 identifies high fitness RNA pseudoknot geometries capable of inducing ribosomal frameshift, while Chapter 7 takes an unbiased approach to evolving sequence and structural elements that promote stop codon readthrough

Forest cover monitoring in Southwestern Ghana with remote sensing and GIS

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesObuasi is one of the major municipalities in southwestern Ghana, Forest resources play a major significant role in the day-to-day activities of the locals due to their high dependency on it. Despite this contribution, the annual rate of current deforestation in Obuasi is about 50 hectares. At this rate the municipality may lose its substantial forest cover completely in the next 25 years. GIS and remote sensing techniques have proven to be efficient ways to monitor forest cover, especially on a large-scale using satellite imagery. In this study, a post-classification comparison change detection algorithm was used to determine the change in forest cover in the 1991-2021 period. The methodology includes a statistical analysis of rainfall and temperature variability for a period of 30years as well as the analysis of perceptions and knowledge of locals on forest modifications. MOLUSCE plugin in QGIS was used to model and generate maps of forest cover and predict future changes in land use/land cover. The land-use/landcover maps showed that between 1991 to 2000 forest areas decline at the rate of 17.1% while another class such as agricultural, built-up, and mining sites has a significant increase of 14%, 4%, and 2% respectively. Between 2000 to 2021, forest areas and agricultural lands decrease from 67% to 60% and 26% to 20% respectively while built-up and mining areas increase from 4% to 12% and 3% to 7% through forest areas remain the dominant landcover class in the area. During the same study period, there was a fluctuation in climatic conditions. Rainfall between 1991 to 2021 has reduced by an amount of 24 mm while temperature has increased to 0.037°C per annum. The majority of the locals believe that cultivated land expansion and mining are the driving forces of forest cover change in the area and the only solutions to these issues are through enrichment planting, and strengthening forest protection laws and mining regulations. Future prediction on forest cover in the area for 2030 map shows that forest areas will be the major contributor of land to other land use/landcover class, henceforth causing it to decline if no intervention is made. These findings can be used to inform conservation and management strategies to mitigate the impact of forest cover change and protect the ecological integrity of forests in the municipality