Thesis (M.E.S.)--The Evergreen State College, 2007. / Title from title screen viewed 1/17/2008. Includes bibliographical references (p. 123-126).

Thesis (M.E.S.)--The Evergreen State College, 2007. / Title from title screen (viewed 1/24/2008). Includes bibliographical references.

Améliorer la durée de vie est un enjeu important qui s'impose lors du déploiement des réseaux de capteurs sans fil (RCsFs). En effet, ces réseaux sont composés par des capteurs autonomes alimentés par des batteries qu'il est difficile de recharger ou remplacer. Le challenge est donc d'assurer le fonctionnement de ces réseaux pendant plusieurs années sans aucune intervention extérieure majeure.Afin de maximiser la durée de vie des RCsFs, nous avons d'abord exploré la possibilité d'introduire plusieurs puits mobiles. Nous avons proposé deux stratégies. La première détermine les positions optimales sur un réseau de petite échelle et la deuxième, basée sur une heuristique, garantit le passage à l'échelle.Nous nous sommes ensuite intéressés aux RCsFs basés sur IPv6 qui utilisent RPL, le nouveau protocole de routage proposé par l'IETF. Nous avons étudié ce protocole, étendu ses capacités pour gérer des puits mobiles et proposé une stratégie de mobilité des puits adaptée permettant de prolonger la durée de vie du réseau.Puis, nous avons proposé une nouvelle approche qui applique le codage de Slepian-Wolf sur les adresses émises dans les RCsFs. L'idée consiste à exploiter la corrélation des adresses garantie par un schéma approprié d'allocation afin de réduire le nombre de bits d'entête transmis au puits et d'améliorer ainsi la durée de vie du réseau.Finalement, nous avons proposé une infrastructure IPv6 hybride pour bâtiments intelligents qui combine les avantages des technologies sans fil et courants porteurs en ligne afin d'améliorer la durée de vie du réseau, sa connectivité et sa robustesse à faible surcoût. / Improving the network lifetime is a very challenging problem that needs to be taken into account during the deployment of wireless sensor networks (WSNs). Indeed, these networks are composed of many autonomous sensors with a limited energy supply provided by batteries which are usually difficult to recharge or replace. The scientific challenge is to ensure the operation of these networks for several years without major external intervention. To maximize the lifetime of WSNs, we first explored the possibility of introducing multiple mobile sinks. We proposed two mobility strategies. The first one provides the optimal placement in a network of small scale. The second one is based on an heuristic algorithm that ensures scalability.We were then interested in IPv6 based WSNs which use the new proposed routing protocol by IETF namely RPL. We studied this protocol, extended its capacity to manage mobile sinks andproposed an appropriate sinks mobility strategy that extends the network lifetime.Next, we proposed a novel approach which consists in applying Slepian-Wolf coding to emitted addresses in WSNs. The basic idea is to exploit the addresses correlation, guaranteed by an appropriate addresses allocation scheme, in order to reduce the header size of packets transmitted to the sink and thus improve the network lifetime.Finally, we proposed an hybrid IPv6 infrastructure for smart buildings which combines the wireless and power line technologies to guarantee energy efficiency and a longer network lifetime.

Le réchauffement régional de l'ouest de la Péninsule Antarctique (WAP) combiné à la diminution attendue de glace de mer et à l'apparition printanière du trou d'ozone pourrait modifier la composition et la structure de la communauté microbienne. De plus, ces variations environnementales pourraient modifier le potentiel de la WAP en tant que puits de CO2. Dans ce contexte, cette thèse visait à évaluer les effets combinés du changement climatique sur la production primaire et sur la composition et la structure de la communauté microbienne de la WAP. Cette thèse visait également à évaluer le rôle de la structure, de la composition, de la production primaire et de la respiration de la communauté microbienne sur les échanges de CO2 entre l'atmosphère et l'océan. Cette étude a premièrement permis de décrire les variations de l'étendue de glace de mer, de l'épaisseur de la couche d'ozone et de la température de surface de l'eau dans la WAP au cours des 30 dernières années (1972-2007) et notamment d'observer le retrait de plus en plus précoce de la glace de mer en relation avec le réchauffement des eaux de la WAP. L'évolution de ces paramètres environnementaux offre une nouvelle fenêtre temporelle de production primaire. Ainsi, cette étude a permis de montrer que la production primaire annuelle a augmenté de 1997 à 2007, et ceci, en relation avec l'anomalie de glace de l'hiver précédent. En effet, la production primaire journalière était négativement et positivement corrélée avec, respectivement, l'étendue de glace de mer et la température de l'eau de septembre à novembre et de février à mars, suggérant que le réchauffement régional de la WAP favorise plus de production primaire durant le printemps et l'automne. En revanche, le retrait précoce de la glace de mer en coïncidence avec l'apparition printanière du trou d'ozone a provoqué l'augmentation de la photoinhibition au printemps (avec 11,6 ± 2,8 % de la production primaire journalière en moyenne). En conséquence, le changement climatique régional de la WAP a, à la fois, un effet positif et un effet négatif sur la production primaire. Cette étude a également permis de décrire la dynamique de la communauté microbienne marine dans l'archipel de Melchior (dans la WAP) de l'automne au printemps 2006. En raison des conditions environnementales extrêmes, l'abondance et la biomasse de la communauté microbienne étaient faibles durant l'automne et l'hiver et dominées par les petites cellules (< 2 µm) et donc par un réseau trophique microbien. En effet, la biomasse phytoplanctonique était faible durant l'automne et l'hiver (avec une concentration moyenne en chlorophylle a, Chl-a, de 0,3 et 0,13 µg l-1, respectivement). La biomasse phytoplanctonique a augmenté au printemps (avec un maximum de Chl-a de 1,13 µg l-1), mais, en dépit des conditions de croissance favorables, est restée faible et le phytoplancton était toujours dominé par de petites cellules (2-20 µm) et donc par le réseau trophique microbien ou multivore. De plus, la disparition précoce de glace de mer durant le printemps 2006 a exposé les eaux de la WAP à de fortes radiations ultraviolettes B (RUVB, 280-320 nm), qui ont eu un effet négatif sur la communauté microbienne des eaux de surface. Cette étude a également mis en évidence la relation existante entre les échanges CO2 et d'O2 entre l'atmosphère et l'océan dans la WAP et la biomasse, la composition, la production primaire et la respiration de la communauté microbienne. Il existait tout d'abord une relation positive entre la concentration en Chl-a et la proportion de diatomées dans la communauté phytoplanctonique. De plus, il existait une corrélation négative significative entre la Chl-a et le ΔpCO2. La production primaire nette de la communauté (NCP) était principalement contrôlée par la production primaire et était négativement et positivement reliée avec le ΔpCO2 et le pourcentage de saturation de l'O2, respectivement, suggérant que la production primaire joue un rôle majeur dans les échanges de CO2 et d'O2 entre l'atmosphère et l'océan dans la WAP. Par ailleurs, le ΔpCO2 moyen au cours des trois années étudiées était de -20,04 ± 44,3 µatm, menant à un puits de CO2 potentiel durant l'été et l'automne dans la région. Le sud de la WAP était un puits potentiel de CO2 (-43,60 ± 39,06 µatm) durant l'automne alors que le nord de la WAP était principalement une source potentielle de CO2 durant l'été ou l'automne (-4,96 ± 37,6 et 21,71 ± 22,39 µatm, respectivement). Les plus fortes concentrations en Chl-a mesurées dans le sud de la WAP pourraient expliquer cette distribution spatiale. / Regional warming in the western Antarctic Peninsula (WAP), along with the expected decrease in sea-ice cover and the seasonal ozone layer breakdown could modify the composition and the structure of the microbial community. In addition, these environmental changes could modify the potential of the WAP as a CO2 sink. In this context, this thesis aimed at evaluating the combined effects of regional climatic changes on the primary production and the composition and structure of the microbial community in the WAP. In a second time, this thesis aimed at evaluating the role of the microbial community structure, composition, primary production and respiration on air-sea CO2 gas exchanges.First, the variations in sea-ice cover, stratospheric ozone layer thickness and sea surface temperature over the last 30 years (1972-2007) were described. Related to the warming of WAP waters, the retreat of sea-ice was happening earlier each decade in the WAP. The observed changes in these environmental parameters offer a new temporal window for primary production. Indeed, the annual primary production increased from 1997 to 2007, in relation with the sea-ice cover anomaly for the previous winter. In addition, daily primary production was negatively and positively correlated to, respectively, sea-ice cover and sea-water temperature from September to November and from February to March, suggesting that regional warming favoured more primary production during spring and fall. On the contrary, the early retreat of sea-ice in spring, in coincidence with the spring ozone layer breakdown, led to an increase in photoinhibition (with an average of 11.6 ± 2.8 % of the daily primary production being photoinhibited). Therefore, regional climatic changes in the WAP had both a positive and a negative impact on primary production.The microbial community variability was also described in the Melchior Archipelago (in the WAP) from fall to spring 2006. Because of the extreme environmental conditions, the microbial community abundance and biomass were low in fall and winter and the community was dominated by small cells (< 2 µm), hence by a microbial food-web. Indeed, phytoplanktonic biomass was low during fall and winter (with respective chlorophyll a concentration, Chl-a, of 0.3 and 0.13 µg l-1). Phytoplankton biomass increased in spring (with a maximum Chl-a of 1.13 µg l-1) but, despite favourable growth conditions, phytoplankton was still dominated by small cells (2-20 µm), hence by a microbial or multivorous food-web. In addition, the early retreat of sea-ice in the spring 2006 exposed the WAP waters to strong ultraviolet B radiations (UVBR, 280-320 nm) that had a negative impact on the microbial community in surface waters.Finally, the relationship between air-sea CO2 and O2 exchanges in the WAP with the phytoplankton community biomass and composition and with the microbial community primary production and respiration was described. A positive relationship existed between Chl-a and the proportion of diatoms in the phytoplankton community. In addition, a negative relationship existed between Chl-a and ΔpCO2. The net community production (NCP) was mainly controlled by primary production and was negatively and positively related to ΔpCO2 and the %O2 saturation, respectively, suggesting that primary production was the main driver of air-sea CO2 and O2 gas exchanges in the WAP. In addition, the average ΔpCO2 for the summers and falls 2002 to 2004 was -20.04 ± 44.3 µatm, leading to a potential CO2 sink during this period in the WAP. The southern WAP was a potential CO2 sink (-43.60 ± 39.06 µatm) during fall while the northern part of the Peninsula was mainly a potential CO2 source during summer and fall (-4.96 ± 37.6 and 21.71 ± 22.39 µatm, respectively). The higher Chl-a concentrations measured in the southern WAP may explain this spatial distribution.

Orientador: Carlos Alfredo Joly / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Biologia / Made available in DSpace on 2018-08-27T11:49:29Z (GMT). No. of bitstreams: 1 Silva_CinthiaAparecida_M.pdf: 9588877 bytes, checksum: dbff1b6c72dbd211b47263ed9583a74c (MD5) Previous issue date: 2015 / Resumo: As florestas tropicais estão entre os ecossistemas terrestres mais diversos e produtivos do planeta, embora ocorram sob solos pobres. Para superar essa condição as plantas adaptam a si mesmas para alocarem biomassa adicional a órgãos onde os recursos são limitantes. Alguns desses órgãos são as raízes finas, raízes responsáveis pela absorção de água e nutrientes do solo. Elas representam um elevado custo de produção para as plantas, mas importante fonte de carbono para o solo. Devido as variações na disponibilidade de recursos influenciarem o estoque e a produção de raízes finas, as expectativas foram de que: i) o estoque e a produção anual de raízes finas aumentariam com a elevação da altitude; ii) a produção de raízes finas seria maior nos períodos de menor umidade; iii) haveria maior biomassa de raiz fina na porção superficial do solo (0-10 cm); iv) o uso de menores tempos de coleta em porções de solo pequenas não afetaria a acurácia do método escolhido. Para testar essas suposições, foram selecionados cinco hectares de Floresta Atlântica conservada. As informações sobre estrutura, composição florística e características do solo foram obtidas de estudos prévios. Em cada um desses hectares, foram instalados 16 coletores para o monitoramento da produção trimestral de raízes finas. O menor estoque e produção total de raízes finas foi encontrado na Floresta Ombrófila Densa Submontana e o maior na Floresta Ombrófila Densa Montana. Os períodos das maiores produções coincidiram com os das maiores temperaturas e precipitações acumuladas e a maior biomassa de raízes finas foi observada na camada de 0-10 cm. A adaptação do método não influenciou significativamente na amostragem das raízes finas. A variável que mais explicou a produção anual foi o conteúdo de água no solo. Com base De acordo com tais resultados, a conclusão foi que as variações climáticas ao longo do gradiente altitudinal não determinaram diretamente o estoque de raízes finas, mas as variações sazonais influenciaram na produção. Quaisquer alterações que venham a ocorrer nas taxas de precipitação, poderão assim, desencadear mudanças significativas na maneira como a Floresta Atlântica aloca carbono, investindo mais em raízes finas do que nos demais órgãos / Abstract: Tropical forests are among the most diverse and productive ecosystems on the planet, however they occur in poor soils. To overcome this condition plants adapt themselves allocating additional biomass to organs where resources are limiting. Some of these organs are the fine roots, roots responsible for absorbing water and nutrients from the soil. They represent a high cost of production to the plants, but at the same time, they are an important source of carbon to the soil. Variations in the resources available can influence stock and production of fine roots and because of that, the expectation was that: i) fine root stock and annual production would increase with elevation; ii) fine roots production would be higher in periods of lower moisture; iii) a higher fine roots biomass would be found in the superficial soil layer (0-10 cm); iv) the use of a short time of sampling in smaller soil portions should not affect the accuracy of the chosen method. To test these hypotheses five plots located in Atlantic Forest along an elevation gradient were selected. The information about the forest structure, floristic composition and soil traits were known from previous studies. Each individual plot had 16 ingrowth cores were installed to monitor the quarterly production of fine roots. Submontane Forest had the smallest stock and annual production of fine roots, while Montane Forest had the highest ones. Periods of higher production coincide with higher temperatures and accumulated rainfall. The first layer of soil, from zero to 10 cm, had the highest fine roots biomass. The method adaptation did not significantly influence the fine roots sampling. The soil water content was the variable which best explained annual production. According to the research results, the conclusion achieved was that the fine roots stock is not directly influenced by climatic variation over elevation, but the seasonal variation influenced the fine roots production. Any possible changes in precipitation rates, may thus trigger significant changes in the way that Atlantic Forest allocates carbon, investing more in fine roots that in other organs / Mestrado / Biologia Vegetal / Mestra em Biologia Vegetal

The ocean is the only cumulative sink of atmospheric CO2. It has absorbed approximately 40% of the CO2 from fossil fuel burning and cement production, lowering atmospheric CO2 and limiting climate change. Here we will examine the regional and global mechanisms controlling the evolution of ocean uptake of this additional carbon from human activities (anthropogenic carbon, Cant) using ocean models and observations. Cant is rapidly injected into the deep ocean, sequestering it from the atmosphere for centuries. It is currently uncertain whether any of this sequestered Cant was absorbed from the atmosphere in the subpolar North Atlantic. Here we present evidence that the upper limb of the ocean’s overturning circulation supplies the subpolar North Atlantic with capacity to absorb Cant from the atmosphere. Using a coupled ocean model, we find that surface freshening of the subpolar North Atlantic reduces the volume available for Cant storage. We also investigate whether global ocean Cant uptake is reduced due to changing ocean circulation, this time across multiple emission scenarios, including scenarios with aggressive emission mitigation. Though it is clear that emission mitigation will reduce the magnitude of the ocean carbon sink, the mechanisms governing the decline in uptake have not been studied in detail. We find that the ocean sink becomes less efficient due to kinematic effects wherein Cant escapes from the surface ocean as atmospheric CO2 plateaus and then declines. In emission scenarios ranging from high to low emissions, projected changes in global Cant uptake due to ocean circulation are small. This is in contrast with the subpolar North Atlantic, where future circulation change plays a important role in the declining Cant uptake.

Urbanization modifies the thermal characteristics of the land and makes way for a succession of transformations in the urban environmental system. This phenomenon, known as Urban Heat Island (UHI), is characterized by elevated temperatures in urban areas that negatively impact on the quality of life and environment in urban areas including, increased emissions of Green House Gases (GHGs) and rising energy consumption. These impacts add to global climate change and thus, mitigating UHI is essential to mitigating global climate change. One GHG, Carbon Dioxide (CO2), accounts for about half of the Earth’s anthropogenic GHG emissions. Terrestrial ecosystems can act as Carbon sinks (C sinks), i.e. natural vegetation reservoirs that absorb more C than they release. Thus, C sinks play an essential and critical function in lowering CO2. Furthermore, providing appropriate C sinks at both the building and urban scales can decrease UHI and contribute to reduction in energy consumption. This study used Landsat 8 imagery of the site, Al Bayt Stadium in Qatar, to investigate the effects of surface UHI by computing the Land Surface Temperature (LST) difference of the site---pre- and post-construction, as well as examine the correlation between natural vegetation abundance and temperature in ten locations within the site’s vicinity. Results show that minimum, maximum and mean LST of the case study area (2014 vs. 2020) decreased 2.80 oC, 5.5 oC and 2.3 oC, respectively, as well as a decreasing trend in the LST as a function of increasing C Sinks. These results demonstrate the importance of introducing C sinks to lower LST and mitigate UHI. Mitigating UHI also has a direct effect on Energy Consumption Balance (ECB). This equilibrium is achieved not only through the introduction of C sinks, but balancing C sinks with high albedo materials and natural ventilation.  Thus, this study also investigated the site’s various design aspects (e.g. cooling technology, structure and surface albedo materials, landscaping) and found that Al Bayt Stadium’s design successfully incorporates strategies to reduce energy consumption at both the urban (macro) and building (micro) scales.

The IoT sensing system plays an important role in the field of the smart city. IoT devices are generally constrained nodes due to their limited power and memory. How to save energy has been a challenge for the scalability of sensing networks. Previous studies introduce the dynamic sink and three dynamic sink deployment strategies. It has been proved by simulation experiments that the sensing network with dynamic sinks can reduce energy consumption. Further investigations on new dynamic sink deployment strategies are needed to explore the full potential of dynamic sinks. This work investigates three new deployment strategies, namely Determinisitic Strategy, Prediction Strategy, and Improved Prediction Strategy. We design experiments with different scenarios and evaluate the packet delivery ratio (PDR) and power consumption performances using emulated IoT devices on the Cooja simulator. The results show that the setups with these three new deployment strategies have good performance in terms of PDR and power consumption. Furthermore, we compare the performance difference between these three new strategies. The Improved Prediction Strategy has advantages over the other two strategies and has application prospects in reality. / IoT-baserade sensorsystem spelar en viktig roll för smarta städer. IoT-enheter är i allmänhet begränsade noder vad gäller till exempel kraftförsörjning och minnesutrymme. Hur man kan spara energi har varit en utmaning för skalbarheten hos sensornätverk. I tidigare studier introduceras dynamiska sänknoder och tre strategier för utplacering av sådana sänknoder. Det har visat sig genom simuleringsexperiment att ett nätverk med dynamiska sänknoder kan minska energiförbrukningen. Ytterligare undersökningar av nya strategier för utplacering av sänknoder behövs för att utforska den fulla potentialen hos dynamiska sänknoder. I det här arbetet undersöks tre nya strategier, nämligen Determinisitic Strategy, Prediction Strategy och Improved Prediction Strategy. Vi utformar experiment med olika scenarier och utvärderar andelen levererade paket (Packet Delivery Ration", PDR) och energiförbrukningen med hjälp av emulerade IoT-enheter i Cooja-simulatorn. Resultaten visar att uppställningarna med dessa tre nya strategier har bra prestanda när det gäller PDR och energiförbrukning. Dessutom jämför vi prestandaskillnaden mellan dessa tre nya strategier. Improved Prediction Strategy har fördelar jämfört med de andra två strategierna och bedöms ha goda tillämpningsmöjligheter i verkliga miljöer.

With warming temperatures, Arctic ecosystems are changing from a net sink to a net sourceof carbon to the atmosphere, but the Arctic’s carbon balance remains highly uncertain. Landscapes are often assumed to be homogeneous when interpreting eddy covariance carbon fluxes, which can lead to biases when gap-filling and scaling-up observations to determine regional carbon budgets. Tundra ecosystems are heterogeneous at multiple scales. Plant functional types, soil moisture, thaw depth, and microtopography, for example, vary across the landscape and influence carbon dioxide (CO₂) and methane (CH4) fluxes. In Chapter 2, I reported results from growing season CO₂ and CH₄ fluxes from an eddy covariance tower in the Yukon-Kuskokwim (YK) Delta in Alaska. I used flux footprint models and Bayesian Markov Chain Monte Carlo (MCMC) methods to unmix eddy covariance observations into constituent landcover fluxes based on high resolution landcover maps of the region. I compared three types of footprint models and used two landcover maps with varying complexity to determine the effects of these choices on derived ecosystem fluxes. I used artificially created gaps of withheld observations to compare gap-filling performance using our derived landcover-specific fluxes and traditional gap-filling methods that assume homogeneous landscapes. I also compared regional carbon budgets scaled up from observations using heterogeneous and homogeneous approaches. Gap-filling methods that accounted for heterogeneous landscapes were better at predicting artificially withheld gaps in CO₂ fluxes than traditional approaches, and there were only slight differences performance between footprint models and landcover maps. I identified and quantified hot spots of carbon fluxes in the landscape (e.g., late growing season emissions from wetlands and small ponds). I resolved distinct seasonality in tundra growing season CO₂ fluxes. Scaling while assuming a homogeneous landscape overestimated the growing season CO₂ sink by a factor of two and underestimated CH₄ emissions by a factor of two when compared to scaling with any method that accounts for landscape heterogeneity. I showed how Bayesian MCMC, analytical footprint models, and high resolution landcover maps can be leveraged to derive detailed landcover carbon fluxes from eddy covariance timeseries. These results demonstrate the importance of landscape heterogeneity when scaling carbon emissions across the Arctic. Climate change is causing an intensification in tundra fires across the Arctic, including the unprecedented 2015 fires in the YK Delta. The YK Delta contains extensive surface waters (approximately 33% cover) and significant quantities of organic carbon, much of which is stored in vulnerable permafrost. Inland aquatic ecosystems act as hot-spots for landscape CO₂ and CH₄ emissions and likely represent a significant component of the Arctic carbon balance, yet aquatic fluxes of CO₂ and CH₄ are also some of the most uncertain. In Chapter 3, I measured dissolved CO₂ and CH₄ concentrations (n = 364), in surface waters from different types of waterbodies during summers from 2016 to 2019. I used Sentinel-2 multispectral imagery to classify landcover types and area burned in contributing watersheds. I developed a model using machine learning to assess how waterbody properties (size, shape, and landscape properties), environmental conditions (O₂ concentration, temperature), and surface water chemistry (dissolved organic carbon composition, nutrient concentrations) help predict in situ observations of CO₂ and CH₄ concentrations across deltaic waterbodies. CO₂ concentrations were negatively related to waterbody size and positively related to waterbody edge effects. CH₄ concentrations were primarily related to organic matter quantity and composition. Waterbodies in burned watersheds appeared to be less carbon limited and had longer soil water residence times than in unburned watersheds. My results illustrated the importance of small lakes for regional carbon emissions and demonstrate the need for a mechanistic understanding of the drivers of greenhouse gasses in small waterbodies. In the Arctic waterbodies are abundant and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. In Chapter 4, I demonstrated how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO₂ and CH₄ fluxes across the YK Delta of south-west AK. I found that waterbody size and contour were strong predictors for aquatic CO₂ emissions, attributing greater than two-thirds of the influence to the scaling model. Small ponds (<0.001 km²) were hotspots of emissions, contributing fluxes several times their relative area, but were less than 5% of the total carbon budget. Small to medium lakes (0.001–0.1 km²) contributed the majority of carbon emissions from waterbodies. Waterbody CH₄ emissions were predicted by a combination of wetland landcover and related drivers, as well as watershed hydrology, and waterbody surface reflectance related to chromophoric dissolved organic matter. When compared to my machine learning approach, traditional scaling methods that did not account for relevant landscape characteristics overestimated waterbody CO₂ and CH₄ emissions by 26%–79% and 8%–53% respectively. This chapter demonstrated the importance of an integrated terrestrial-aquatic approach to improving estimates and uncertainty when scaling carbon emissions in the arctic. In order to understand carbon feedbacks with the atmosphere and predict climate change, we need to develop methods to model and scale up carbon emissions. Gridded datasets of carbon fluxes are used to benchmark Earth system models, attribute changes in rates of atmospheric concentrations of greenhouse gases, and project future climate change. There are two main approaches to deriving gridded datasets of carbon fluxes and global or regional carbon budgets: bottom-up scaling, and top-down atmospheric inversions. There is often divergence between approaches, with carbon budgets calculated from bottom-up and top-down studies rarely overlapping. The resulting uncertainty in carbon budgets calculated from either approach is more pronounced in high-latitudes. One of the challenges with combining bottom-up models and comparing top-down models is the variable spatial resolutions used in each approach. In Chapter 5, I applied flux scaling models from earlier chapters to create bottom-up carbon budgets at very high resolution (10 m) for the entire YK Delta domain. I used ERA5 land reanalysis data to extend the flux models to 2012-2015 and 2017 growing seasons to coincide with airborne observations of atmospheric CO₂ and CH₄ concentrations from NASA CARVE and Arctic-CAP campaigns. I progressively coarsened remote sensing imagery for the region to 30 m, 90 m, 250 m, and 1 km to create coarser landcover maps and corresponding bottom-up carbon budgets. The high resolution bottom-up models, when convolved with concentration footprints, produced simulated atmospheric enhancements that were similar to observed atmospheric enhancements. There was little change coarsening to 30 m and 90 m, but simulated atmospheric enhancements and especially carbon budgets were quite different at 250 m and 1 km spatial resolution. The changes with resolution were largely the result of an increase in area mapped as wetlands and shrub tundra, and less area mapped as small waterbodies and lichen tundra. Coarser resolution bottom-up scaling consistently overestimated CH4 budgets. By evaluating flux models against atmospheric observations, I was able to diagnose missing components such as inland water carbon emissions and times when the scaling models overestimated emissions to missing seasonal dynamics. This dissertation combined novel uses of statistical techniques with a high density of field observations to yield process-level understanding of carbon cycling that could be applied to scaling-up carbon emissions. By merging terrestrial and aquatic perspectives and concurrently mapping ecosystem landcovers and disturbances at high spatial resolution, I avoided common sources of uncertainty in carbon budgets such as double-counting of areas. I investigated how we represent the landscape in terms of both spatial resolution and the level of landscape heterogeneity, and determined the effects of these choices on carbon fluxes and budget estimates. By comparing to the atmosphere, I evaluated the validity of different approaches to modeling carbon fluxes in the Arctic. Together, the chapters in this dissertation provided a holistic study of carbon cycling in the Arctic.

Accurately modeling climate and the impacts of climate change relies heavily on extensive observations. Soil moisture is a critical variable in this regard, as it influences energy partitioning, regulates the water cycle, directly affects vegetation dynamics, modulates terrestrial carbon sinks and sources, and overall plays a vital role in the land-atmosphere interactions and feedback. This work aims to improve the quality of available surface soil moisture data and its complementary dataset -- vegetation optical depth (since both are derived from the same satellite measurements). The datasets developed in the scope of this study fill the gap in the available observational data pool as unique, long-term, consistent datasets developed based on remote sensing data. These datasets were created with the help of machine learning tools, in particular, deep dense neural networks. The distinctive characteristics of the utilized approach include (1) decomposition of the signal into seasonal and residual parts and training a neural network to match the residuals; (2) applying a special transfer learning training scheme that allows adjusting the features of a trained neural network to a slightly different input that ultimately permits merging the non-compatible directly and disjoint satellite sources into a consistent dataset; (3) using an ensemble of neural networks to assess the data uncertainty. Upon development, the datasets were profoundly validated vs. in-situ soil moisture measurements for soil moisture and biomass and photosynthesis-related datasets for vegetation optical depth. The consistent and long-term nature of the created datasets allowed for the study of decadal trends in soil moisture and the potential drivers for its dynamics. Finally, this study presents two showcases of the datasets used for constraining models -- as data assimilated in a simple carbon cycle model and as an emergent constraint in an ensemble of global climate models. The vegetation optical depth dataset was used in a simple carbon cycle model and demonstrated how it can constrain unobserved respiration flux and carbon pools. In this project's scope, the role of information content, data quality, and local conditions is assessed. The soil moisture dataset is used to constrain global climate models' projections of future soil moisture change by constraining the past soil moisture change range. Altogether, this study proposes a robust methodology for merging data from different sources into a consistent long-term dataset (provided that at least a short overlap in data exists for transfer learning). The analysis of the soil moisture dataset reveals that the regions of drying and wetting dynamics exist globally and can be identified with statistically significant trends in soil moisture. The dynamics are studied seasonally, revealing the contradicting trends in soil moisture in some regions (for example, in Europe, wetting in spring and drying in summer) and persistent trends throughout the year for others (for example, drying in the Mediterranean). Similarly, the local drivers of the soil moisture change are established. The soil moisture change is mainly driven by variations in precipitation for dry regions and in temperature in wet regions with the rising role of vegetation dynamics, especially in high latitudes. Finally, the vegetation optical depth data has proven its high potential in constraining respiration flux and carbon pools, significantly improving the carbon cycle model predictions in the regions subjected to interannual variability in meteorological forcing conditions and vegetation response.