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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 |
