Harry McCaughey

Scaling up flux measurements for the boreal forest using aircraft-tower combinations

Fluxes of carbon dioxide, water vapor, sensible heat, and momentum obtained over the boreal forest from the Twin Otter aircraft and six tower-based systems are compared. These measurements were collected as part of the Boreal Ecosystem-Atmosphere Study (BOREAS) during three intensive field campaigns between May 25 and September 17, 1994. The representativeness of the tower-based measurements collected during BOREAS is discussed. Even though the net radiation from aircraft- and tower-based systems agreed well, in general, the aircraft tended to observe larger latent heat and smaller sensible heat fluxes than the towers. The CO2 fluxes from the aircraft were substantially less than from the tower, while the differences were relatively small for the momentum fluxes. The relationships between aircraft and tower-based flux measurements obtained by making repeated runs past various towers are used to scale up tower-based fluxes to a 16×16 km2 area near Prince Albert, Saskatchewan. It is demonstrated that except for a couple of cases primarily due to rapidly changing radiation conditions, this combination of measurements provides regional flux estimates of momentum, CO2, and sensible and latent heat similar to those obtained by flying a grid pattern over the area.

Climatic and Phenological Controls of the Carbon and Energy Balances of Three Contrasting Boreal Forest Ecosystems in Western Canada

Seasonal and interannual variability in the carbon and energy cycles of boreal forests are controlled by the interaction of climate, ecophysiology and plant phenology. This study analyses eddy-covariance data from mature trembling aspen, black spruce and jack pine stands in western Canada. The seasonal cycles of the surface carbon and energy balances were tightly coupled to the seasonal cycle of soil temperature. The contiguous carbon-uptake period was ∼50 days longer for the black spruce and jack pine stands than the trembling aspen stand, with 30 days difference in spring and 20 days difference in autumn. The black spruce and jack pine carbon-uptake period spanned the warm season, with gross ecosystem photosynthesis beginning during spring thaw and continuing until air temperature dropped to below freezing in autumn. In contrast, the trembling aspen carbon-uptake period was determined by the timing of leaf emergence and senescence, which occurred well after spring thaw and before autumn freeze. Regression analysis identified spring temperature as the primary factor controlling annual net ecosystem production at all three sites, through its influence on the onset of the growing season. Precipitation and soil water content had significant but secondary influences on the annual carbon fluxes. The impact of spring warming on annual net ecosystem production was 2–3 times greater at the deciduous-broadleaf than the evergreen-coniferous sites, confirming the high sensitivity of boreal deciduous-broadleaf forests to spring warming. The analysis confirmed the pivotal role of phenology in the response of northern ecosystems to climate variability and change.

Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy‐scale photosynthesis

The proliferation of digital cameras co-located with eddy covariance instrumentation provides new opportunities to better understand the relationship between canopy phenology and the seasonality of canopy photosynthesis. In this paper we analyze the abilities and limitations of canopy color metrics measured by digital repeat photography to track seasonal canopy development and photosynthesis, determine phenological transition dates, and estimate intra-annual and interannual variability in canopy photosynthesis. We used 59 site-years of camera imagery and net ecosystem exchange measurements from 17 towers spanning three plant functional types (deciduous broadleaf forest, evergreen needleleaf forest, and grassland/crops) to derive color indices and estimate gross primary productivity (GPP). GPP was strongly correlated with greenness derived from camera imagery in all three plant functional types. Specifically, the beginning of the photosynthetic period in deciduous broadleaf forest and grassland/crops and the end of the photosynthetic period in grassland/crops were both correlated with changes in greenness; changes in redness were correlated with the end of the photosynthetic period in deciduous broadleaf forest. However, it was not possible to accurately identify the beginning or ending of the photosynthetic period using camera greenness in evergreen needleleaf forest. At deciduous broadleaf sites, anomalies in integrated greenness and total GPP were significantly correlated up to 60 days after the mean onset date for the start of spring. More generally, results from this work demonstrate that digital repeat photography can be used to quantify both the duration of the photosynthetically active period as well as total GPP in deciduous broadleaf forest and grassland/crops, but that new and different approaches are required before comparable results can be achieved in evergreen needleleaf forest.

Hydrological effects on carbon cycles of Canada's forests and wetlands

The hydrological cycle has significant effects on the terrestrial carbon (C) balance through its controls on photosynthesis and C decomposition. A detailed representation of the water cycle in terrestrial C cycle models is essential for reliable estimates of C budgets. However, it is challenging to accurately describe the spatial and temporal variations of soil water, especially for regional and global applications. Vertical and horizontal movements of soil water should be included. To constrain the hydrology-related uncertainty in modelling the regional C balance, a three-dimensional hydrological module was incorporated into the Integrated Terrestrial Ecosystem Carbon-budget model (InTEC V3.0).We also added an explicit parameterization of wetlands. The inclusion of the hydrological module considerably improved the model’s ability to simulate C content and balances in different ecosystems. Compared with measurements at five flux-tower sites, the model captured 85% and 82% of the variations in volumetric soil moisture content in the 0–10 cm and 10– 30cmdepths during the growing season and84%of the interannual variability in the measuredCbalance. The simulations showed that lateral subsurface water redistribution is a necessary mechanism for simulating water table depth for both poorly drained forest and peatland sites. Nationally, soil C content and their spatial variability are significantly related to drainage class. Poorly drained areas are important C sinks at the regional scale, however, their soil C content and balances are difficult to model and may have been inadequately represented in previous C cycle models. The InTEC V3.0 model predicted an annual net C uptake by Canada’s forests and wetlands for the period 1901–1998 of 111.9 Tg C yr-1, which is 41.4 Tg C yr-1 larger than our previous estimate (InTEC V2.0). The increase in the net C uptake occurred mainly in poorly drained regions and resulted from the inclusion of a separate wetland parameterization and a detailed hydrologic module with lateral flow in InTEC V3.0.

Modeling the Response of Canopy Stomatal Conductance to Humidity

Humidity of air is a key environmental variable in controlling the stomatal conductance (g) of plant leaves. The stomatal conductance‐humidity relationships employed in the Ball‐Woodrow‐Berry (BWB) model and the Leuning model have been widely used in the last decade. Results of independent evaluations of the two models vary greatly. In this study, the authors develop a new diagnostic parameter that is based on canopy water vapor and CO2 fluxes to assess the response of canopy g to humidity. Using eddy-covariance flux measurements at three boreal forest sites in Canada, they critically examine the performance of the BWB and the Leuning models. The results show that the BWB model, which employs a linear relationship between g and relative humidity (hs), leads to large underestimates of g when the air is wet. The Leuning model, which employs a nonlinear function of water vapor pressure deficit (Ds), reduced this bias, but it still could not adequately capture the significant increase of g under the wet conditions. New models are proposed to improve the prediction of canopy g to humidity. The best performance was obtained by the model that employs a power function of Ds, followed by the model that employs a power function of relative humidity deficit (1 2 hs). The results also indicate that models based on water vapor pressure deficit generally performed better than those based on relative humidity. This is consistent with the hypothesis that the stomatal aperture responds to leaf water loss because water vapor pressure deficit rather than relative humidity directly affects the transpiration rate of canopy leaves.

Modeling acclimation of photosynthesis to temperature in evergreen conifer forests.

• In this study, we used a canopy photosynthesis model which describes changes in photosynthetic capacity with slow temperature-dependent acclimations. • A flux-partitioning algorithm was applied to fit the photosynthesis model to net ecosystem exchange data for 12 evergreen coniferous forests from northern temperate and boreal regions. • The model accounted for much of the variation in photosynthetic production, with modeling efficiencies (mean > 67%) similar to those of more complex models. The parameter describing the rate of acclimation was larger at the northern sites, leading to a slower acclimation of photosynthesis to temperature. The response of the rates of photosynthesis to air temperature in spring was delayed up to several days at the coldest sites. Overall photosynthesis acclimation processes were slower at colder, northern locations than at warmer, more southern, and more maritime sites. • Consequently, slow changes in photosynthetic capacity were essential to explaining variations of photosynthesis for colder boreal forests (i.e. where acclimation of photosynthesis to temperature was slower), whereas the importance of these processes was minor in warmer conifer evergreen forests.

Prediction of Macronutrients at the Canopy Level Using Spaceborne Imaging Spectroscopy and LiDAR Data in a Mixedwood Boreal Forest

Information on foliar macronutrients is required in order to understand plant physiological and ecosystem processes such as photosynthesis, nutrient cycling, respiration and cell wall formation. The ability to measure, model and map foliar macronutrients (nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg)) at the forest canopy level provides information on the spatial patterns of ecosystem processes (e.g., carbon exchange) and provides insight on forest condition and stress. Imaging spectroscopy (IS) has been used particularly for modeling N, using airborne and satellite imagery mostly in temperate and tropical forests. However, there has been very little research conducted at these scales to model P, K, Ca, and Mg and few studies have focused on boreal forests. We report results of a study of macronutrient modeling using spaceborne IS and airborne light detection and ranging (LiDAR) data for a mixedwood boreal forest canopy in northern Ontario, Canada. Models incorporating Hyperion data explained approximately 90% of the variation in canopy concentrations of N, P, and Mg; whereas the inclusion of LiDAR data significantly improved the prediction of canopy concentration of Ca (R2 = 0.80). The combined used of IS and LiDAR data significantly improved the prediction accuracy of canopy Ca and K concentration but decreased the prediction accuracy of canopy P concentration. The results indicate that the variability of macronutrient concentration due to interspecific and functional type differences at the site provides the basis for the relationship observed between the remote sensing measurements (i.e., IS and LiDAR) and macronutrient concentration. Crown closure and canopy height are the structural metrics that establish the connection between macronutrient concentration and IS and LiDAR data, respectively. The spatial distribution of macronutrient concentration at the canopy scale mimics functional type distribution at the site. The ability to predict canopy N, P, K, Ca and Mg in this study using only IS, only LiDAR or their combination demonstrates the excellent potential for mapping these macronutrients at canopy scales across larger geographic areas into the next decade with the launch of new IS satellite missions and by using spaceborne LiDAR data.

Spatially simulating changes of soil water content and their effects on carbon sequestration in Canada's forests and wetlands.

The variations of soil water content (SWC) and its influences on the carbon (C) cycle in Canada’s forests and wetlands were studied through model simulations using the Integrated Terrestrial Ecosystem Carbon (InTEC) model. It shows that Canada’s forests and wetlands experienced spatially and temporally heterogeneous changes in SWC from 1901 to 2000. SWC changes caused average NPP to decrease 40.8 Tg C yr−1 from 1901 to 2000, whereas the integrated effect of non-disturbance factors (climate change, CO2 fertilization and N deposition) enhanced NPP by 9.9%. During 1981–2000, the reduction of NPP caused by changes in SWC was 58.1 Tg C yr−1 whereas non-disturbance factors together caused NPP to increase by 16.6%. SWC changes resulted in an average increase of 4.1 Tg C yr−1 in the net C uptake during 1901–2000, relatively small compared with the enhancement in C uptake of 50.2 Tg C yr−1 by the integrated effect of non-disturbance factors. During 1981–2000, changes in SWC caused a reduction of 3.8 Tg C yr−1 in net C sequestration whereas the integrated factors increased net C sequestration by 54.1 Tg C yr−1. Increase in SWC enhanced C sequestration in all ecozones.

Influences of vegetation structure and elevation on CO2 uptake in a mature jack pine forest in Saskatchewan, Canada

Carbon dioxide, water vapour, and energy fluxes vary spatially and temporally within forested environments. However, it is not clear to what extent they vary as a result of variability in the spatial distribution of biomass and elevation. The following study presents a new methodology for extracting changes in the structural characteristics of vegetation and elevation within footprint areas, for direct comparison with eddy covariance (EC) CO2 flux concentrations. The purpose was to determine whether within-site canopy structure and local elevation influenced CO2 fluxes in a mature jack pine (Pinus banksiana Lamb.) forest located in Saskatchewan, Canada. Airborne light detection and ranging (lidar) was used to extract tree height, canopy depth, foliage cover, and elevation within 30 min flux footprints. Within-footprint mean structural components and elevation were related to 30 min mean net ecosystem productivity (NEP) and gross ecosystem production (GEP). NEP and GEP were modeled using multiple regressio...

Testing the robustness of predictive models for chlorophyll generated from spaceborne imaging spectroscopy data for a mixedwood boreal forest canopy

The amount of chlorophyll in a leaf influences photosynthetic potential and can be an indicator of the overall condition of a plant, including its stress level and nutritional status. Hence, it is important to understand the spatial and temporal variation of chlorophyll concentration. Imaging spectroscopy (IS) has made it possible to estimate chlorophyll at leaf and canopy levels. Spaceborne imaging spectrometers offer the possibility of estimating chlorophyll concentration at larger spatial scales and at lower cost than from direct sampling. We undertook this study in a mixedwood boreal forest to test the robustness of predictive models generated using Hyperion data for predicting chlorophyll concentration of data sets from different locations collected in different years. Among the group of indices tested, the derivative chlorophyll index (DCI) (DCI = D705/D722) and the maximum derivative of the red-edge divided by the derivative of 703 nm (Dmax(680–750))/D703) emerged as the best predictors of chlorophyll concentration across space and through time. When the canopy level chlorophyll predictive models of DCI and Dmax(680–750))/D703 derived from Hyperion data were applied to other years’ remote-sensing data acquired by airborne and spaceborne sensors, DCI explained 71%, 63%, and 6% and Dmax(680–750))/D703 explained 61%, 54%, and 8% of the variation in chlorophyll in 2002, 2004, and 2008, respectively, with prediction errors ranging from 11.7% to 14.6%. Two-variable models generated using 2005 Hyperion data were not as robust for predicting chlorophyll concentration from other years. Two models were found to explain more than half of the variance in chlorophyll concentration for the 2004 data only. Single and two-variable models applied to 2008 chlorophyll data provided poor predictions. The presence of multiple species creates a gradient in the chlorophyll concentration, which makes it possible to predict chlorophyll concentration. The gradient also affects the performance of predictive models generated using data from a different year. However, differences in sensors may also affect model performance. Our results suggest that predictive models obtained from Hyperion data are robust in predicting chlorophyll concentration within the same site through time and also at different sites across sensors.