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Agroforestry plantations and urban habitats contribute importantly to atmospheric volatile compound fluxes in densely populated areas. Simulation of emissions from such habitats is associated with several key challenges, including high spatial heterogeneity due to habitat fragmentation and high diversity of planted tree species. On the other hand, plants in urban habitats and in agroforestry plantations commonly receive more nutrients and water than species in natural communities, resulting in higher production and potentially greater capacity for volatile production per unit of land area. This chapter reviews the strategies for simulation of biogenic volatile organic compound (BVOC) fluxes from urban habitats and agroforestry plantations and provides an outline for parameterization of volatile emission models for densely populated areas with high vegetation fragmentation and large number of gardened, often exotic, tree species.

The increasing food demand as well as the need to predict the impact of warming climate on vegetation makes it critical to find the best tools to assess crop production and carbon dioxide (CO₂) exchange between the land and atmosphere. Photosynthesis is a good indicator of crop production and CO₂ exchange. Chlorophyll fluorescence (ChF) is directly related to photosynthesis. ChF can be measured at leaf-scale using active techniques and at field-scales using passive techniques. The measurement principles of both techniques are different. In this study, three overarching questions about ChF were addressed: Q1) How water, nutrient and ambient light conditions determine the relationships between photosynthesis and ChF? Which is the optimum irradiance level for detecting water and nutrient deficit conditions with ChF?; Q2) which are the limits within which active and passive techniques are comparable?; and Q3) What is the seasonal relationship between photosynthesis and ChF when nitrogen is the limiting factor? To address these questions, two main experiments were conducted: Exp1) Concurrent photosynthesis and ChF light-response curves were measured in camelina and wheat plants growing under (i) intermediate-light and (ii) high-light conditions respectively. Plant stress was induced by (i) withdrawing water, and (ii) applying different nitrogen levels; and Exp2) coincident active and passive ChF measurements were made in a wheat field under different nitrogen treatments. The results indicated ChF has a direct relationship with photosynthesis when water or nitrogen drives the relationship. This study demonstrates that the light level at which plants were grown was optimum for detecting water and nutrient deficit with ChF. Also, the results showed that for leaf-average-values, active measurements can be used to better understand the daily and seasonal behavior of passive ChF. Further, the seasonal relation between photosynthesis and ChF with nitrogen stress was not a simpl

We measured the carbon isotopic composition (δ13C) of ecosystem respiration (δ13CR) in a subalpine forest across four growing seasons to examine whether patterns in δ13CR were consistent with those expected based on leaf‐level gas‐exchange theory, and in agreement with past studies of the relation between δ13CR and climate conducted across broad geographic regions. Conventional trends (i.e., less negative δ13CR with increased vapor pressure deficit (VPD) and air temperature (TAIR), and decreased soil moisture (θ)) were observed when we focused on the driest portions of average‐wetness years and when δ13CR was positively correlated with nighttime ecosystem respiration (RE). Nonconventional trends (i.e., more negative δ13CR with decreased θ , and increased VPD and TAIR) were observed under specific climatic conditions (e.g., late snowmelt; extreme TAIR late in the growing season), and when δ13CR was negatively correlated with RE. These nonconventional trends were independently corroborated using δ13C of extracted sugars from needles of dominant tree species at the site. Our results clearly demonstrate that the commonly reported relations between δ13CR and climate may break down depending on the interactions among environmental conditions. Efforts to model and predict the variability of δ13CR under changing climatic variables must characterize and parameterize the effects of unique combinations of weather conditions and variable hydrologic regimes, in combination with the susceptibility of photosynthetic isotope discrimination to extreme air temperatures.

More accurate projections of future carbon dioxide concentrations in the atmosphere and associated climate change depend on improved scientific understanding of the terrestrial carbon cycle. Despite the consensus that U.S. terrestrial ecosystems provide a carbon sink, the size, distribution, and interannual variability of this sink remain uncertain. Here we report a terrestrial carbon sink in the conterminous U.S. at 0.63 pg C yr−1 with the majority of the sink in regions dominated by evergreen and deciduous forests and savannas. This estimate is based on our continuous estimates of net ecosystem carbon exchange (NEE) with high spatial (1 km) and temporal (8-day) resolutions derived from NEE measurements from eddy covariance flux towers and wall-to-wall satellite observations from Moderate Resolution Imaging Spectroradiometer (MODIS). We find that the U.S. terrestrial ecosystems could offset a maximum of 40% of the fossil-fuel carbon emissions. Our results show that the U.S. terrestrial carbon sink varied between 0.51 and 0.70 pg C yr−1 over the period 2001–2006. The dominant sources of interannual variation of the carbon sink included extreme climate events and disturbances. Droughts in 2002 and 2006 reduced the U.S. carbon sink by ∼20% relative to a normal year. Disturbances including wildfires and hurricanes reduced carbon uptake or resulted in carbon release at regional scales. Our results provide an alternative, independent, and novel constraint to the U.S. terrestrial carbon sink.

• Understanding controls over plant–atmosphere CO2 exchange is important for quantifying carbon budgets across a range of spatial and temporal scales. In this study, we used a simple approach to estimate whole-tree CO2 assimilation rate (ATree) in a subalpine forest ecosystem. • We analysed the carbon isotope ratio (δ13C) of extracted needle sugars and combined it with the daytime leaf-to-air vapor pressure deficit to estimate tree water-use efficiency (WUE). The estimated WUE was then combined with observations of tree transpiration rate (E) using sap flow techniques to estimate ATree. Estimates of ATree for the three dominant tree species in the forest were combined with species distribution and tree size to estimate and gross primary productivity (GPP) using an ecosystem process model. • A sensitivity analysis showed that estimates of ATree were more sensitive to dynamics in E than δ13C. At the ecosystem scale, the abundance of lodgepole pine trees influenced seasonal dynamics in GPP considerably more than Engelmann spruce and subalpine fir because of its greater sensitivity of E to seasonal climate variation. • The results provide the framework for a nondestructive method for estimating whole-tree carbon assimilation rate and ecosystem GPP over daily-to weekly time scales.

Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate–carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO2 exchange with the atmosphere across biomes and continents are lacking. Here we present data describing the relationships between net ecosystem exchange of carbon (NEE) and climate factors as measured using the eddy covariance method at 125 unique sites in various ecosystems over six continents with a total of 559 site-years. We find that NEE observed at eddy covariance sites is (1) a strong function of mean annual temperature at mid- and high-latitudes, (2) a strong function of dryness at mid- and low-latitudes, and (3) a function of both temperature and dryness around the mid-latitudinal belt (45◦N). The sensitivity of NEE to mean annual temperature breaks down at ∼16 ◦C (a threshold value of mean annual temperature), above which no further increase of CO2 uptake with temperature was observed and dryness influence overrules temperature influence.

Sensor networks promise to transform and expand environmental science. However, many technological dif- ficulties must be overcome to achieve this potential. Partnerships of ecologists with computer scientists and engineers are critical in meeting these challenges. Technological issues include promoting innovation in new sensor design, incorporating power optimization schemes, integrating appropriate communication protocols, streamlining data management and access, using innovative graphic and statistical analyses, and enabling both scientists and the public to access the results. Multidisciplinary partnerships are making major contribu- tions to technological advances, and we showcase examples of this exciting new technology, as well as new approaches for training researchers to make effective use of emerging tools.

Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site‐years, 10 biomes, and includes two large‐scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models’ ability to simulate the seasonal cycle of CO2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model‐data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.

The quantification of carbon fluxes between the terrestrial biosphere and the atmosphere is of scientific importance and also relevant to climate-policy making. Eddy covariance flux towers provide continuous measurements of ecosystem-level exchange of carbon dioxide spanning diurnal, synoptic, seasonal, and interannual time scales. However, these measurements only represent the fluxes at the scale of the tower footprint. Here we used remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to upscale gross primary productivity (GPP) data from eddy covariance flux towers to the continental scale. We first combined GPP and MODIS data for 42 AmeriFlux towers encompassing a wide range of ecosystem and climate types to develop a predictive GPP model using a regression tree approach. The predictive model was trained using observed GPP over the period 2000–2004, and was validated using observed GPP over the period 2005–2006 and leave-one-out cross-validation. Our model predicted GPP fairly well at the site level. We then used the model to estimate GPP for each 1 km×1 km cell across the U.S. for each 8-day interval over the period from February 2000 to December 2006 using MODIS data. Our GPP estimates provide a spatially and temporally continuous measure of gross primary production for the U.S. that is a highly constrained by eddy covariance flux data. Our study demonstrated that our empirical approach is effective for upscaling eddy flux GPP data to the continental scale and producing continuous GPP estimates across multiple biomes. With these estimates, we then examined the patterns, magnitude, and interannual variability of GPP. We estimated a gross carbon uptake between 6.91 and 7.33 Pg C yr−1 for the conterminous U.S. Drought, fires, and hurricanes reduced annual GPP at regional scales and could have a significant impact on the U.S. net ecosystem carbon exchange. The sources of the interannual variability of U.S. GPP were dominated by these e

There is considerable interest in modeling isoprene emissions from terrestrial vegetation, because these emissions exert a principal control over the oxidative capacity of the troposphere.

Carbon flux models based on light use efficiency (LUE), such as the MOD17 algorithm, have proved difficult to parameterize because of uncertainties in the LUE term, which is usually estimated from meteorological variables available only at large spatial scales. In search of simpler models based entirely on remote-sensing data, we examined direct relationships between the enhanced vegetation index (EVI) and gross primary productivity (GPP) measured at nine eddy covariance flux tower sites across North America. When data from the winter period of inactive photosynthesis were excluded, the overall relationship between EVI and tower GPP was better than that between MOD17 GPP and tower GPP. However, the EVI/GPP relationships vary between sites. Correlations between EVI and GPP were generally greater for deciduous than for evergreen sites. However, this correlation declined substantially only for sites with the smallest seasonal variation in EVI, suggesting that this relationship can be used for all but the most evergreen sites. Within sites dominated by either evergreen or deciduous species, seasonal variation in EVI was best explained by the severity of summer drought. Our results demonstrate that EVI alone can provide estimates of GPP that are as good as, if not better than, current versions of the MOD17 algorithm for many sites during the active period of photosynthesis. Preliminary data suggest that inclusion of other remote-sensing products in addition to EVI, such as the MODIS land surface temperature (LST), may result in more robust models of carbon balance based entirely on remote-sensing data