Previous work demonstrates conflicting evidence regarding the influence of snowmelt timing on forest net ecosystem exchange (NEE). Based on 15years of eddy covariance measurements in Colorado, years with earlier snowmelt exhibited less net carbon uptake during the snow ablation period, which is a period of high potential for productivity. Earlier snowmelt aligned with colder periods of the seasonal air temperature cycle relative to later snowmelt. We found that the colder ablation-period air temperatures during these early snowmelt years lead to reduced rates of daily NEE. Hence, earlier snowmelt associated with climate warming, counterintuitively, leads to colder atmospheric temperatures during the snow ablation period and concomitantly reduced rates of net carbon uptake. Using a multilinear-regression (R-2=0.79, P<0.001) relating snow ablation period mean air temperature and peak snow water equivalent (SWE) to ablation-period NEE, we predict that earlier snowmelt and decreased SWE may cause a 45% reduction in midcentury ablation-period net carbon uptake.

Over the last millennium peatlands have accumulated significant carbon stores. Drainage for agricultural use has been widespread and has altered the functioning of these mires: shifting them towards carbon release. Recently, in recognition of the range of ecosystem services derived from these landscapes peatland restoration projects have been initiated. Carbon storage is often cited amongst the aims of these projects, especially since the inclusion of rewetting wetlands in the Kyoto Protocol. However, little is known about the effects of ditch blocking on CO2 fluxes, particularly in Molinia caerulea dominated peatlands, a species common on degraded peatlands which tolerates a range of water table depths. This thesis aims firstly to quantify CO2 fluxes from a drained Molinia caerulea dominated blanket bog and to improve understanding of the temporal and spatial controls on these fluxes and secondly, to quantify the immediate effects of ditch blocking. Closed chamber measurements of net ecosystem exchange and partitioned below-ground respiration from control-restored paired sites were collected over the growing seasons immediately pre- (2012) and post-restoration (2013/2014). These flux data were coupled with remotely sensed data quantifying vegetation phenology and structure with a fine resolution (daily/cm) over large extents (annual/catchment). Although temporal variation in water table depth was not related to CO2 fluxes, the seasonal average related to vegetation composition suggesting raising water tables may promote a change in vegetation composition within these species-poor ecosystems. The distribution of water table depths, vegetation composition and CO2 fluxes did not vary with proximity to drainage ditches despite their prominence. An empirical model suggests in a drained state these peatlands are CO2 sources, indicating carbon previously accumulated is gradually being lost. Data suggest restoration does not always significantly affect water tables and consequently CO2 fluxes in the short-term. Where shallower water tables were maintained during dry conditions photosynthesis decreased and heterotrophic respiration increased: enhancing carbon release. Research undertaken during atypical weather has been unable to determine if restoration will be able to raise water tables sufficiently to protect the existing peat store and promote the vegetation change required to reinstate CO2 sequestration in the longer-term.

The terrestrial CO2 exchange in the Arctic plays an important role in the global carbon (C) cycle. The Arctic ecosystems, containing a large amount of organic carbon (C), are experiencing ongoing warming in recent decades, which is affecting the C cycling and the feedback interactions between its different components. To improve our understanding of the atmosphere-ecosystem interactions, the Greenland Ecosystem Monitoring (GEM) program measures ecosystem CO2 exchange and links it to biogeochemical processes. However, this task remains challenging in northern latitudes due to an insufficient number of measurement sites, particularly covering full annual cycles, but also the frequent gaps in data affected by extreme conditions and remoteness. Combining ecosystem models and field observations we are able to study the underlying processes of Arctic CO2 exchange in changing environments. The overall aim of the research is to use data-model approaches to analyse the patterns of C exchange and their links to biological processes in Arctic ecosystems, studied in detail both from a measurement and a modelling perspective, but also from a local to a pan-arctic scale. In Paper I we found a compensatory response of photosynthesis (GPP) and ecosystem respiration (Reco), both highly sensitive to the meteorological drivers (i.e. temperatures and radiation) in Kobbefjord, West Greenland tundra. This tight relationship led to a relatively insensitive net ecosystem exchange (NEE) to the meteorology, despite the large variability in temperature and precipitations across growing seasons. This tundra ecosystem acted as a consistent sink of C (-30 g C m-2), except in 2011 (41 g C m-2), which was associated with a major pest outbreak. In Paper II we estimated this decrease of C sink strength of 118-144 g C m-2 in the anomalous year (2011), corresponding to 1210-1470 tonnes C at the Kobbefjord catchment scale. We concluded that the meteorological sensitivity of photosynthesis and respiration were similar, and hence compensatory, but we could not explain the causes. Therefore, in Paper III we used a calibrated and validated version of the Soil-Plant-Atmosphere model to explore full annual C cycles and detail the coupling between GPP and Reco. From this study we found two key results. First, similar metrological buffering to growing season reduced the full annual C sink strength by 60%. Second, plant traits control the compensatory effect observed (and estimated) between gross primary production and ecosystem respiration. Because a site-specific location is not representative of the entire Arctic, we further evaluated the pan-Arctic terrestrial C cycling using the CARDAMOM data assimilation system in Paper IV. Our estimates of C fluxes, pools and transit times are in good agreement with different sources of assimilated and independent data, both at pan-Arctic and local scale. Our benchmarking analysis with extensively used Global Vegetation Models (GVM) highlights that GVM modellers need to focus on the vegetation C dynamics, but also the respiratory losses, to improve our understanding of internal C cycle dynamics in the Arctic. Data-model approaches generate novel outputs, allowing us to explore C cycling mechanisms and controls that otherwise would not have been possible to address individually. Also, discrepancies between data and models can provide information about knowledge gaps and ecological indicators not previously detected from field observations, emphasizing the unique synergy that models and data are capable of bringing together.

Cape Bounty, Melville Island is a partially vegetated High Arctic landscape with three main plant communities: polar semi-desert (47% of the study area), mesic tundra (31%) , and wet sedge meadows (7%). The objective of this research was to relate biophysical measurements of soil, vegetation, and CO2 exchange rates in each vegetation type to high resolution satellite data from IKONOS-2, extending plot level measurements to a landscape scale. Field data was collected through six weeks of the 2008 growing season. Two IKONOS images were acquired, one on July 4th and the other on August 2nd. Two products were generated from the satellite data: a land-cover classification and the Normalized Difference Vegetation Index (NDVI). The three vegetation types were found to have distinct soil and vegetation characteristics. Only the wet sedge meadows were a net sink for CO2; soil respiration tended to exceed photosynthesis in the sparsely vegetated mesic tundra and polar semi-desert. Scaling up the plot measurements by vegetation type area suggested that Cape Bounty was a small net carbon source (0.34 ± 0.47 g C m-2 day-1) in the summer of 2008. NDVI was strongly correlated with percent vegetation cover, vegetation volume, soil moisture, and moderately with soil nitrogen, biomass, and leaf area index (LAI). Photosynthesis and respiration of CO2 both positively correlated with NDVI, most strongly when averaged over the season. NDVI increased over time in every vegetation type, but this change was not reflected in any significant measured changes in vegetation or CO2 flux rates. A simple spatial model was developed to estimate Net Ecosystem Exchange (NEE) at every pixel on the satellite images based on NDVI, temperature and incoming solar radiation. It was found that the rate of photosynthesis per unit NDVI was higher early in the growing season. The model estimated a mean flux to the atmosphere of 0.21 g C m-2 day-1 at the time of image acquisition on July 4th, and -0.07 g C m-2 day-1 (a net C sink) on August 2nd. The greatest uncertainty in the relationship between NDVI and CO2 flux was associated with the polar semi-desert class. / Thesis (Master, Geography) -- Queen's University, 2011-12-28 23:27:34.824

Agriculture contributes significantly to national and global greenhouse gas (GHG) inventories but there is considerable control over management decisions and changes in production methods could lead to a significant reduction and possible mitigation of emissions from the sector. For example, conservation tillage practices have been suggested as a method of sequestering atmospheric carbon dioxide (CO2), however, many questions remain unanswered regarding the short-term efficacy of the production method and knowledge gaps exist regarding possible interactions with essential nutrient cycles, and the production of non-CO2 GHGs, such as nitrous oxide (N2O). Between autumn 2005 and 2009, a micrometeorological flux system was used to determine net CO2 and N2O exchange from an annual cropping system situated on clay soil in the Red River Valley of southern Manitoba. Four plots (4-ha each) were independently evaluated and planted to corn in 2006 and faba bean in 2007; in 2008, two spring wheat plots were monitored. As well, during the non-growing season in 2006-2007 following corn harvest, a second micrometeorological flux system capable of simultaneously measuring stable C isotopologue (12CO2 and 13CO2) fluxes was operated at the site. Tillage intensity and crop management practices were examined for their influence on GHG emissions. Significant inter-annual variability in CO2 and N2O fluxes as a function of crop and related management activities was observed. Tillage intensity did not affect GHG emissions from the site. After accounting for harvest removals, the net ecosystem C budgets were 510 (source), 3140 (source) and -480 (sink) kg C/ha/year for the three respective crop years, summing to a three-year loss of 3170 kg C/ha. Stable C isotope flux measurements during the non-growing season following corn harvest indicated that approximately 70 % and 20 – 30 % of the total respiration flux originated from crop residue C during the fall of 2006 and spring of 2007, respectively. The N2O emissions at the site further exacerbated the net global warming potential of this annual agroecosystem.

Feedbacks between the climate system and the high-latitude carbon cycle will substantially influence the intensity of future climate change. It is therefore crucial that the net ecosystem exchange of CO2 (NEE) between the high-latitude land surface and the atmosphere is accurately quantified, where NEE refers to the difference between ecosystem respiration (R) and photosynthesis (gross ecosystem exchange, GEE): NEE=-GEE+R in umol/m^2/s. NEE can only be directly measured over areas of 1 km^2 through eddy covariance, and modeling approaches such as the Vegetation Photosynthesis Respiration Model (VPRM) are required to upscale NEE. VPRM is a remote sensing based model that calculates R as a linear function of air temperature (Ta) when air temperature is above a given threshold (Tlow), and sets respiration to a constant value when Ta<Tlow. GEE is estimated according to remote sensing observations of vegetation indices, shortwave radiation, air temperature, and soil moisture. Although in situ findings have shown that snow and Arctic species composition have a substantial influence on high-latitude NEE, model estimates of high-latitude NEE have typically been generated without Arctic-specific vegetation classes, and without using remote sensing observations to represent the effects of snow on NEE. The hypothesis driving this work was therefore that uncertainty in estimates of high-latitude NEE could be reduced by representing the influences of Arctic vegetation classes and snow. The central objectives were to determine feasible approaches for reducing uncertainty in VPRM estimates of NEE by representing the influences of snow and Arctic vegetation, create PolarVPRM accordingly, and analyze inter-annual variability in PolarVPRM estimates of high-latitude North American NEE (2001-2012). The associations between snow and NEE, and the potential to describe these influences on NEE using remote sensing observations, were examined using time lapse camera observations of snow cover area (SCA) and eddy covariance measurements of NEE from Daring Lake, Northwest Territories, Canada. Analyses indicated good agreement between SCA derived from camera, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) observations. SCA was also found to influence the timing and magnitude of NEE. MODIS SCA was therefore incorporated into VPRM, and VPRM was calibrated using eddy covariance and meteorological observations collected in 2005 at Daring Lake. VPRM was run through years 2004-2007 over both Daring Lake and Ivotuk, Alaska, USA, using four model formulations, three of which represented the effects of SCA on respiration and/or photosynthesis, and another which did not use MODIS SCA. Comparisons against eddy covariance observations indicated that uncertainty was reduced in VPRM estimates of NEE when respiration was calculated as a linear function of soil temperature when SCA>50%, and as a linear function of air temperature when SCA<50%, thereby reflecting the influence of snow on decoupling soil/air temperatures. Representing the effect of SCA on NEE therefore reduced uncertainty in VPRM estimates of NEE. In order to represent spatial variability in high-latitude estimates of NEE due to vegetation type, Arctic-specific vegetation classes were created for PolarVPRM by combining and aggregating two existing vegetation classifications: the Synergetic Land Cover Product and the Circumpolar Arctic Vegetation Map. Levene's test indicated that the PolarVPRM vegetation classes divided the pan-Arctic region into heterogeneous distributions in terms of net primary productivity, and passive microwave derived estimates of snow and growing season influences on NEE. A non-parametric statistical approach of Alternating Conditional Expectations found significant, non-linear associations to exist between passive microwave derived estimates of snow and growing season drivers of NEE. Furthermore, the shape of these associations varied according to the vegetation class over which they were examined. Further support was therefore provided to the idea that uncertainty in model estimates of NEE could be reduced by calculating snow and growing season NEE separately within each vegetation class. PolarVPRM estimates of NEE in 2001-2012 were generated at a three hourly and 1/6 x 1/4 degree resolution across polar North America (55-170 W, 55-83 N). Model calibration was conducted over three sites: Daring Lake, Ivotuk, and Atqasuk, Alaska, USA. Model validation was then conducted by comparing PolarVPRM estimates of year-round daily average NEE to non-gap-filled eddy covariance observations of daily average NEE acquired over the three calibration sites, as well as six other Arctic sites. PolarVPRM performed well over all sites, with an average mean absolute error (MAE) of 0.20 umol/m^2/s, and had diminished error rates when the influence of SCA on respiration was explicitly represented. Error analysis indicated that peak growing season GEE was underestimated at Barrow because GEE at this site showed a stronger response to the amount of incoming shortwave radiation than at the calibration site, suggesting that PolarVPRM may underestimate GEE over wetland and barren vegetated regions. Despite these uncertainties, PolarVPRM was found to generate more accurate estimates of monthly and three-hourly NEE relative to eddy covariance observations than two established models, FLUXNET Model-Tree Ensemble (MTE) and CarbonTracker. Relative to eddy covariance observations and PolarVPRM estimates, MTE tended to overestimate snow season respiration, and CarbonTracker tended to overestimate the amount of midday photosynthesis. Analysis of PolarVPRM output across North America (north of 55 N) found an increase in net annual carbon efflux over over time (2001-2012). Specifically, increased rates of respiration are estimated when soil and air temperatures are warmer. Although increases in growing season vegetation indices and air temperature enable greater photosynthetic uptake by Arctic vegetation, forests and shrublands uptake less CO2 in the middle of the growing season when air temperatures rise above the physiological optima for photosynthesis. As a result, PolarVPRM estimated a decline in net photosynthetic uptake over time. Overall, PolarVPRM output indicates that North American regions north of 55 N are losing strength as a carbon sink in response to rising air temperatures.

Agriculture contributes significantly to national and global greenhouse gas (GHG) inventories but there is considerable control over management decisions and changes in production methods could lead to a significant reduction and possible mitigation of emissions from the sector. For example, conservation tillage practices have been suggested as a method of sequestering atmospheric carbon dioxide (CO2), however, many questions remain unanswered regarding the short-term efficacy of the production method and knowledge gaps exist regarding possible interactions with essential nutrient cycles, and the production of non-CO2 GHGs, such as nitrous oxide (N2O). Between autumn 2005 and 2009, a micrometeorological flux system was used to determine net CO2 and N2O exchange from an annual cropping system situated on clay soil in the Red River Valley of southern Manitoba. Four plots (4-ha each) were independently evaluated and planted to corn in 2006 and faba bean in 2007; in 2008, two spring wheat plots were monitored. As well, during the non-growing season in 2006-2007 following corn harvest, a second micrometeorological flux system capable of simultaneously measuring stable C isotopologue (12CO2 and 13CO2) fluxes was operated at the site. Tillage intensity and crop management practices were examined for their influence on GHG emissions. Significant inter-annual variability in CO2 and N2O fluxes as a function of crop and related management activities was observed. Tillage intensity did not affect GHG emissions from the site. After accounting for harvest removals, the net ecosystem C budgets were 510 (source), 3140 (source) and -480 (sink) kg C/ha/year for the three respective crop years, summing to a three-year loss of 3170 kg C/ha. Stable C isotope flux measurements during the non-growing season following corn harvest indicated that approximately 70 % and 20 – 30 % of the total respiration flux originated from crop residue C during the fall of 2006 and spring of 2007, respectively. The N2O emissions at the site further exacerbated the net global warming potential of this annual agroecosystem.

The impact of cold climate on physical and biological processes, especially the role of air and soil temperature in recovering photosynthesis and transpiration in boreal forests, was investigated in a series of studies. A process-based ecosystem model (CoupModel) considering atmospheric, soil and plant components was evaluated and developed using Generalized Likelihood Uncertainty Estimation (GLUE) and detailed measurements from three different sites. The model accurately described the variability in measurements within days, within years and between years. The forcing environmental conditions were shown to govern both aboveground and belowground processes and regulating carbon, water and heat fluxes. However, the various feedback mechanisms between vegetation and environmental conditions are still unclear, since simulations with one model assumption could not be rejected when compared with another. The strong interactions between soil temperature and moisture processes were indicated by the few behavioural models obtained when constrained by combined temperature and moisture criteria. Model performance on sensible and latent heat fluxes and net ecosystem exchange (NEE) also indicated the coupled processes within the system. Diurnal and seasonal courses of eddy flux data in boreal conifer ecosystems were reproduced successfully within defined ranges of parameter values. Air temperature was the major limiting factor for photosynthesis in early spring, autumn and winter, but soil temperature was a rather important limiting factor in late spring. Soil moisture and nitrogen showed indications of being more important for regulating photosynthesis in the summer period. The need for systematic monitoring of the entire system, covering both soil and plant components, was identified as a subject for future studies. The results from this modelling work could be applied to suggest improvements in management of forest and agriculture ecosystems in order to reduce greenhouse gas emissions and to find adaptations to future climate conditions. / QC 20110921 / the Nitro-Europe project

This thesis presents the first modeling study on long-term carbon dynamics for the University of Guelph Elora Agricultural Research Station and the Environment Canada Borden Forest Research Station at the daily and half-hourly time-step. The daily version of the CENTURY (DayCENT) model and the Carbon- and Nitrogen-coupled Canadian Land Surface Scheme (CN-CLASS) model were validated for quantifying the effects of agricultural management and component respiration on the carbon budget. DayCENT indicated that conventional tillage (CT) enhanced the annual heterotrophic respiration relative to no-till (NT) by 38.4, 93.7 and 64.2 g C m-2 yr-1 for corn, soybean and winter wheat, respectively. The seasonal variation of total soil organic carbon (SOC) pool was greater in CT than NT due to tillage effects on carbon transfer from the active surface SOC pool to the active soil SOC pool at a rate of 50-100 g C m-2 yr-1. NT accounted for a 10.7 g C m-2 yr-1 increase in the slow SOC pool (20-year turnover time) at a site in Elora, Ontario, Canada. I found that the plant phenology algorithms used in CN-CLASS were not constructed and validated for crop growth, resulting in a high degree of uncertainty in the simulations. Therefore, I designed and tested a new agricultural module for CN-CLASS. The regression analysis indicated that the new crop module improved the net ecosystem productivity (NEP) simulation for a cornfield, with the coefficient of determination (r2) of annual NEP increasing from 0.51 in the original CN-CLASS to 0.78 in the modified version of the model. I verified CN-CLASS to simulate the dynamics of component respiration for tracing the contributions from litterfall, SOC and root respiration in a deciduous mixedwood forest in Borden, Ontario, Canada. The model estimated that the annual ecosystem CO2 respiration was 1366 g C m-2 yr-1, contributed by heterotrophic respiration (57%), maintenance respiration (37%) and growth respiration (6%). The annual accumulated soil respiration was estimated at 782 g C m-2 yr-1, which was dominated by CO2 emissions from soil organic matter (60%). The base respiration rates required further verification based on field measurements. Based on the verified modeling approach in this thesis, the modeling core of DayCENT can be constructed as an integral platform for Agriculture and Agri-Food Canada National Carbon and Greenhouse Gas Accounting and Verification System. The crop phenological module in CN-CLASS allows us to conduct further agricultural studies concerning global carbon budget and environmental change. The validated respiration algorithms in CN-CLASS would be helpful in developing global biological CO2 transport model for tracing emission sources. / Natural Science and Engineering Research Council of Canada

Net ecosystem carbon dioxide exchange (NEE) was measured using the eddy covariance (EC) technique at a wetland tundra-sedge fen near Churchill, Manitoba, Canada during two consecutive growing seasons (2007 and 2008). Mean daily NEE at the fen (DOY 157-254) was -3.5 (± 0.26 S.E.) g CO2 m-2 d-1 in 2007 and -4.6 (± 0.36) g CO2 m-2 d-1 in 2008. The fen was a net carbon dioxide (CO2) sink during both the 2007 and 2008 growing seasons of -343 (± 79) and -450 (± 87) g CO2 m-2, respectively. Mean air temperature during the summer (June 1-August 31) was about 1°C greater than the historical average (1971-2000) in 2007 and about 2°C greater in 2008. Growing season precipitation was 107.5 mm below normal in 2007 and 359.5 mm above normal in 2008. These data suggest that if future climate change brings warmer temperatures and near-to-above average precipitation maintaining the water table near the surface, similar subarctic ecosystems will experience increased gross ecosystem productivity enhancing CO2 sequestration during the growing season.